URBAN TRANSPORT Journal. Vol 13 No.1 September 2014 INSTITUTE OF URBAN TRASNPORT (INDIA)

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1 URBAN TRANSPORT Journal Vol 13 No.1 September 2014 INSTITUTE OF URBAN TRASNPORT (INDIA) 1

2 URBAN TRANSPORT Journal Editorial Board Dr. Sanjay Gupta (Chairman) School of Planning & Architecture, Delhi Dr.Geetam Tiwari Indian Institute of Technology, Delhi Shri Piyush Kansal RITES Ltd, Gurgaon Dr. Ashish Verma Indian Institute of Science, Bengaluru Dr.Pawan Kumar Town & Country Planning Organization, Delhi Shri C.L.Kaul(Convenor) All communication pertaining to submission of papers for publication in the journal may be sent by at the following address: Executive Secretary Institute of Urban Transport (India) 1st Floor, AnandVihar Metro Station Building, (Entry adjacent to Gate No 1)Delhi Phones: (91) & 40 (D) (91) (10 lines) Ext , 740 Telefax:91) clkaul@iutindia.org Website: All rights reserved Views expressed in the papers in this Journal are those of the authors and not necessarily the views of the Institute. 2

3 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 From the Editor s Desk The present issue of the journal comprises of eight technical papers presenting diverse spectrum of research themes such as global walkability index, event day effect on pedestrian behavior, effect of lane friction on speed of NMV s, service quality determinants for public transport and use intention, framework for estimating carbon footprint of commuters, modeling mode choice behaviour of commuters, roundabout entry capacity model, framework for development of advanced traveler information system and institutional & financial strengthening of intermediate public transport services in Indian cities. The first paper on Application of Global Walkability Index (GWI: Case study of Bangalore, India is an attempt to demonstrate an application of nine point street rating tool developed by the World Bank and Clean Air Initiative (CAI) Asia to four streets with major pedestrian footfall in the metropolitan city of Bangalore, India. It highlights the advantages of having the tool in a smartphone application format with connectivity to a global crowd map for application by professionals working towards street improvement designs and aid creating a global database of knowledge. The second paper on Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City focusses on the pedestrian flow behaviour on normal week day and event day captured t hr ou gh video recording of pedestrian movement in CBD area of Vadodara city. The paper highlights the variations in pedestrian flow behavior during normal and religious event day in terms of volume, speed, density, space and level of service of pedestrian flow in both conditions. The author reiterates the need for TSM actions to improve pedestrian movement on an event day. The third paper on Effect of Lane Friction on Speed of Non-Motorized Vehicles is an interesting attempt on the NMV (bicycle and cycle rickshaw together) flows on urban roads based on an empirical study carried out in Roorkee city for three roads, all of two-lanes with either one-directional or two-direction traffic. The paper concludes that the speeds were influenced by share of NMV flows. In particular on a road segment with no friction from opposite direction, the NMV speed is reduced with an increase in total flow; on segment with friction from opposite direction it is reduced with increase in opposing direction flows, while on segment with all frictions it is reduced with NMV, total and opposing direction flows The fourth paper on Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non- Commuters in India The paper attempts to address the prime objective of identifying the service quality of determinants for commuters and non-commuters and their influence on use intention. This paper is based on empirical studies conducted in the cities of Delhi, Mumbai, Allahabad and Jabalpur amongst the commuters and non-commuters. It was found that both the user groups gave weightage to availability and tangibles factors as important service quality determinants though in different order along with empathy, responsiveness for commuters and safety, integration for non-commuters respectively.

4 The fifth paper on Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes demonstrates a method for estimating the carbon footprint of commuting and apply it to the public transport systems existing in Delhi. It is based on an empirical study carried out in Delhi of the transit commuters of available modes to estimate the carbon foot prints for different mode- combinational trips (trip profile including access, egress and main line haul mode). The authors reiterate that carbon footprints assessment has the potential to provide insights into the potential impact of different policies. The sixth paper on Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi reiterates that mode choice forms an integral part of travel demand modeling as it gives a complete insight to the mode choice preferences of the commuters. In their paper the authors attempts to model the mode choice of commuters in Delhi based on discrete multinomial logit model. The paper is based on an empirical study of various localities of Delhi and uses thirteen explanatory variables for modeling mode choice behavior. It also quantifies the value of travel time separately for motorized and non motorized mode of commuters. The seventh paper on Selection of Roundabout Entry Capacity Model for Indian Condition emphasizes that evaluating the capacity of roundabout is an important element in the planning and design of such facilities. In this paper an empirical approach using regression analysis was used to develop a roundabout entry-capacity model for Indian conditions. It was observed that the entry capacity of an approach of a roundabout is dependent on the circulating flow in front of that approach Also the critical gap and follow-up time values recommended in HCM (2010) are not applicable to Indian conditions.the paper concludes that the capacity based on U.S. model gave results close to that of field entry capacity model. The eight paper on Framework for Development of Advanced Traveler Information System: A Case Study for Chandigarh City presents a comprehensive framework comprising of system architecture, development methodology and salient features of a GIS based ATIS for city of Chandigarh City, India. The author emphasizes that the suggested system is able to provide the information about the basic facilities of the city and help the users in planning and decision making about their trips by providing shortest routes, nearest facilities and bus routes. The last paper on Institutional and Financial Strengthening of Intermediate Public Transport Services in Indian Cities focuses on the Intermediate Public Transport like 3 wheelers, auto rickshaws, tempos and tata magic that caters the daily urban trips in Indian cities. This paper identifies the key challenges faced by this sector and provides recommendations for addressing these challenges. Dr. Sanjay Gupta

5 URBAN TRANSPORT Journal Volume 13 No.1 September 2014 Contents Application of Global Walkability Index (GWI): Case Study Bangalore, India Neelakshi Joshi, Prof. R. Shankar and Prof. Dr. Ing Helmut Bott 1 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City Hardik S Sukhadia, Sanjay M Dave, Jiten Shah, Dipak Rathva 14 Effect of Lane Friction on Speed of Non-Motorized Vehicles Prasham Khadaiya and Rajat Rastogi 26 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India Dr. Vibhuti Tripathi and Gunjan Nema 39 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes Kirti Bhandari, Mukti Advani, Purnima Parida 57 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi Minal and Ch.Ravi Sekhar 67 Selection of Roundabout Entry Capacity Model for Indian Condition Abdullah Ahmad, Srinath Mahesh and Rajat Rastogi 78 Framework for Development of Advanced Traveler Information System: A Case Study for Chandigarh City Bhupendra Singh, Ankit Gupta, Sanjeev Suman 87 Institutional and Financial Strengthening of Intermediate Public Transport Services in Indian Cities Anindita Ghosh and Kanika Kalra 96

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7 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 APPLICATION OF GLOBAL WALKABILITY INDEX (GWI): CASE STUDY BANGALORE, INDIA Neelakshi Joshi, Prof. R. Shankar and Prof. Dr. Ing Helmut Bott* Abstract: Global Walkability Index (GWI) is a nine point street rating tool developed by the World Bank and Clean Air Initiative (CAI) Asia. This paper applies GWI to four streets with major pedestrian footfall in the metropolitan city of Bangalore, India. The possibility of adding a tenth criterion i.e. of Environment Quality is explored. Data collected is further analyzed and is found to generate a score of out of 50 for the city, which is low. Furthermore, the advantages of having the tool in a smartphone application format with connectivity to a global crowdmap are discussed. This paper aims to encourage those working towards street improvement designs to widely apply this emerging tool and help create a global database of knowledge. Keywords: GlobalWalkability IndexWalkscore, Pedestrians, Mobility 1.0 INTRODUCTION Indian cities, in recent years, have experienced a steady shift towards private vehicular ownership. As per Census of India 2011, urban car ownership stands at 9.7% and two-wheeler ownership at 35.2%. Though these are still low compared to global standards, their increasing numbers on Indian roads are leading to major congestion problems and increase in air and sound pollution. In Bangalore, transport is responsible for 42% of all the air pollution (TERI, 2010). Furthermore, pedestrians share in road accidents is also on the rise, being as high as 44% in Bangalore (MoUD, 2008). Municipal funding primarily caters to road widening and flyover projects, without adequate improvement of non-motorized facilities. This is evident from the transport projects undertaken under Jawaharlal Nehru National Urban Renewal Mission (JNNURM) between Around 56% of the transport projects have been either for road widening or flyover construction, 33% for public transport and 5% for improving parking facilities. The remaining 5% is categorized as others and pedestrians may or may not be included in this segment (IIHS, 2011). This apathy towards pedestrians coupled with an unsafe and unpleasant walking environment further encourages people to shift to motorized modes. Walkability is a measure of the ease of walking from one place to another. Definitions differ as contexts change. Some attribute it to the nature of land use: The extent to which the built environment is friendly to the presence of people living, shopping, visiting, enjoying or spending time in an area (Abbey, 2005) while others attribute it to physical infrastructure, comfort and safety from crime (Krembeck et.al, 2006). It is true that mixed land use, facilitating ease of walking to work and recreation, is an essential tool for enhancing walk trips. However, in the context of India, most streets are intrinsically mixed use and applying mixed land use parameters like Walkscore generate good results. Walkscore is an internet based service that analyzes streets geospatially based on the distance of various amenities from the said location.high scores are generated for mixed use areas where people can walk to access various facilities like shops, schools, restaurants. However, the real problem of bad walking environment, lack of comfort and safety can be gauged well by indices that take a more microscopic approach. * Indian Institute of Technology, Roorkee and University of Stuttgart neelakshijoshi@gmail.com

8 Application of Global Walkability Index (GWI): Case Study Bangalore, India 2.0 GLOBAL WALKABILITY INDEX (GWI) Global Walkability Index (GWI) was developed by Holly Krambeck and Jitendra Shah (Krembeck et.al, 2006). It rates streets and the walking environment based on nine predefined criteria. These criteria cover aspects of safety, security and comfort (see Table 1). Streets are rated on a scale of 0-5, 0 being a complete absence of the said parameter and 5 indicating excellent conditions. Length of the street and pedestrian count further contribute towards developing a rating. This aims to quantify the problems of bad pedestrian environment and furthermore develop a universal scale for measuring and comparing these. Clean Air Initiative (CAI), Asia conducted field walkability surveys, following the GWI parameters in 13 Asian cities namely Cebu (Philippines), Colombo (Sri Lanka), Davao (Philippines), Ha Noi (Viet Nam), Ho Chi Minh City (Viet Nam), Hong Kong, China (People s Republic of China), Jakarta (Indonesia), Karachi (Pakistan), Kathmandu (Nepal), Kota (India), Lanzhou (PRC), Manila (Philippines), and Ulaanbaatar (Mongolia) (Leather, 2011). It eliminated the variables of pedestrian count and length of street surveyed as including these often resulted in streets with high pedestrian counts but bad infrastructure to get high scores. Besides the nine point criteria adopted by CAI, the authors further explored adding a tenth criteria of Environment Quality measured in terms of air quality and sound pollution levels as pedestrians are directly exposed to this during their walk trips Table 1: Global Walkability Index (GWI) Parameters No. Parameter Score Description 1 Walking Path Modal Conflict 2 Availability Of Walking Paths 3 Availability Of Crossings 1 Significant conflict that makes walking impossible 2 Walking possible, but dangerous and inconvenient 3 Some conflict walking is possible, but not convenient 4 Minimal conflict, mostly between non-motorized modes 5 No conflict between pedestrians and other modes 1 Pedestrian walkways required but not available 2 Available but highly congested, bad maintenance 3 Available but congested, needs better maintenance 4 Available, sometimes congested, well maintained 5 Walkways not required: people can safely walk on roads 1 Controlled crossings is > 500m and average speed is high 2 Controlled crossings is between m and average speed is around 40 Kmph 3 Controlled crossings is between m and average speed is Kmph 4 Controlled crossings is between m and average speed is Kmph 2

9 Application of Global Walkability Index (GWI): Case Study Bangalore, India No. Parameter Score Description 5 Vehicles and pedestrians coexist safely 4 Grade Crossing Safety 5 Motorist Behavior 1 High probability of accident; very high crossing time 2 Dangerous- pedestrian faces some risk of being hurt by other modes and crossing time is high 3 Difficult to ascertain dangers posed to pedestrians but the time available for crossing is less 4 Safe exposure time is less and time available for crossing more 5 Very safe other modes present no danger to pedestrians 1 High traffic disrespect to pedestrians 2 Traffic disrespect and rarely pedestrians get priority 3 Motorists sometimes yield 4 Obey laws and sometimes yield to pedestrians 6 Amenities 1 No Amenities 7 Disability Infrastructure 5 Obey traffic laws and almost always yield to pedestrians 2 Little amenities at some locations 3 Limited number of provisions for pedestrians 4 Good amenities for major length 5 Excellent amenities such as lighting, cover from sun and rain making walking a pleasant experience 1 No infrastructure for disabled people is available 2 Limited infrastructure but not in usable condition 3 Present but in poor condition and not well placed 4 Present, in good condition, but poorly placed 5 Present, in good condition, and well placed 8 Obstructions 1 Infrastructure is completely blocked by obstructions 9 Security from Crime 2 Significantly inconvenienced. Effective width <1m 3 Mildly inconvenienced; effective width is < or = 1 meter 4 Minor inconvenience. Effective width is > 1m 5 There are no obstructions 1 Very dangerous pedestrians highly susceptible to crime 2 Dangerous pedestrians are at some risk of crime 3

10 Application of Global Walkability Index (GWI): Case Study Bangalore, India No. Parameter Score Description 3 Difficult to ascertain perceived degree of security 4 Secure pedestrians at minimal crime risk 5 Very secure pedestrians at virtually no risk of crime 10 Environment Quality* 1 Pedestrian exposed to very high air and sound pollution 2 Air pollution high and sound between 80-90db 3 Air pollution moderate, sound between 60-80db 4 No perceptible air pollution, sound between 50-60bd 5 Pleasant air quality, Sound <50db Source: Krambeck& Shah, 2006 and *Author CAI also developed a smartphone app called Walkability making it easy to conduct field surveys using smartphones (see Image 1). The rating criteria has been simplified and presented in an easy to understand format. The idea is to enable citizens to easily rate their surrounding streets. The ratings posted on the app appear on a global crowd map which automatically generates city and country score based on these ( 3.0 METHODOLOGY Four streets in the metropolitan city of Bangalore were identified for conducting the walkability survey (see Figure 2). High pedestrian count in these streets was the primary criteria for selection (see Table 2). These streets were also found to have heavy motor traffic gauged through high values of Passenger Car Units (PCU). Incidentally, all four are primarily commercial streets with active shop frontages. Walkability surveys were conducted between 11:00 am - 12:00 noon on four subsequent Sundays between to Sunday noon has been identified as the peak for pedestrians in these areas (RITES, 2011). Figure 1: Screenshot of Walkability App Source: tps://itunes.apple.com/us/app/cai-asiawalka-bility-app/id ?mt=8 Figure 1: Streets identified for study, Bangalore 4

11 Application of Global Walkability Index (GWI): Case Study Bangalore, India Sr. Name of Street Table 2: Streets identified for study, Bangalore Length of street surveyed (m) Pedestrian Count/Ho ur PCU/Hour One way 1. 9th Main, Jayanagar 240m 5,797 89,376 Yes 2. Brigade Road 355m 5,198 59,832 Yes 3. Sampige Road, Malleshwaram 404m 3,110* 68,045 Yes 4. Gandhi Bazaar Road, Baswangudi 660m 2,578 1,02,678 No Source: Rites India and *Author All ten criteria described in Table 1 were taken into consideration for rating streets. Each criteria was scored on a scale of 0 to 5, 0 being the worst and 5 being the best situation. Each street was rated overall out of 50 points. The average of all four streets suggests the city score. Sound pollution levels were measured employing Noise Meter app that helps record the decibel levels in an area. Air pollution levels are difficult to gauge as current smartphone do not have sensors to measure this. Hence, air pollution was qualitatively analyzed by the author based on a comparative analysis of the four streets. Results were tabulated and analyzed to develop and overall city score. Furthermore, the results generated using GWI were compared against Walkscore for the same streets. Walkscore for a particular street was generated by entering its location on Scores are generated automatically by a geospatial analysis of distance of various amenities like restaurants, schools, offices etc from the said location. 4.0 LIMITATIONS GWI primarily relies on the perception of the surveyor making it a qualitative tool for analysis. Despite providing a scale for rating various aspects of the walking environment, there is scope for individual interpretation and variation. Bias of the surveyor can further impact the rating. The 10th criteria on Environment Quality suggests using in-built sensors of a smart phone to quantify noise and air pollution. Training surveyors and providing interpretation manuals for online users are some steps taken by Clean Air Asia to generate consistent rating. 5.0 SURVEY Surveys were conducted rating streets as per the GWI parameters. Furthermore air and noise exposure levels were also captured. Photographs were taken to capture certain aspects of the survey better. Following is a detailed account of each road: th Main Road, Jayanagar 9th Main Road is an active commercial street in Bangalore located in the residential neighbourhood of Jayanagar. It records the highest pedestrian counts. Though some effort has gone into separating pedestrians from motor vehicles, providing benches, installing dustbins and street lighting the streets are unsafe to cross as cars freely pass through the area and do not yield to pedestrians. Also, there is encroachment by shopkeepers and vendors forcing pedestrians to walk on the road with cars. Noise levels are high owing to constant honking by cars to alert pedestrians. 5

12 Application of Global Walkability Index (GWI): Case Study Bangalore, India Table 3: Walkability Survey, 9th Main Road, Jayanagar Criteria Remark Score Pedestrian pathways exist but people end up walking on road due to Walking Path encroachment (see Figure 3a) Modal Conflict 2 Availability of m wide on both sides Walking Paths Encroached by local vendors 3 Trees, dustbins and lampposts also obstruct walking Availability of Crossings Grade Crossing Safety Motorist Behavior Amenities Disability Infrastructure Obstructions Security from Crime Walking Environment No defined crossing except at junctions (250m) People jaywalk to cross 2 Zebra crossings obstructed by railings (see Figure 3b) Traffic is not controlled hence pedestrian has to negotiate the road himself 2 Persistent honking No priority to pedestrians 2 Physical separation from main road Street lights and dustbins at 100m (see Figure 3c) 3 Benches in open areas Footpath heights are not negotiable by wheelchair Frequent obstructions 0 Encroachments from shops Existing physical barriers like lamp posts, dustbins and trees 2 Vibrant mixed use Improvement in lighting is needed 3 Noise level at 82 db Air pollution levels high 2 Total 21/50 Figure 3a: Pedestrian on Main Road Figure 3b: Blocked Zebra Crossing Figure 3c: Pedestrian Amenities and Obstructions Source: Author 5.2 Brigade Road Brigade Road is the main commercial street in Bangalore city. It is located in the heart of the central business district. Pedestrians on this street are restricted to footpaths that are clearly inadequate to hold such large pedestrian volumes. The central spine is for cars making it difficult to cross the road. Air and sound pollution from the cars and motor cycles further affect the riding environment. Shopkeepers often encroach a part of the footpath. There are no places to sit and relax. Furthermore, because of pedestrians packed on the footpaths, pick pocketing and harassment of women is common. 6

13 Application of Global Walkability Index (GWI): Case Study Bangalore, India Table 4: Walkability Survey, Brigade Road Criteria Remark Score Walking Path Modal Conflict Availability of Walking Paths Availability of Crossings Grade Crossing Safety Motorist Behavior Amenities Disability Infrastructure Obstructions Security from Crime Walking Environment Source: Author Separate pedestrian walkways High density in peak hours 3m-3.2m wide on both sides (see Figure 4a) Encroached local vendors (see Figure 4b) Over crowded in peak hours Crossing exists at 150m but is not respected Signalled crossing at 300m Cars take priority Difficult to cross, high waiting time (see Figure 4c) Honking at pedestrians Cars take priority, do not respect crossings Physical separation from main road Street lights at regular intervals No seats or dustbins Footpath heights are not negotiable by wheelchair Plenty of obstructions Excessive crowd for disable person to feel comfortable Encroachment from shops Existing physical barriers like lamp posts Vibrant commercial use Pick pocketing in the crowd Noise level at 87dB Air pollution levels high Total 20/50 Figure 4a: Pedestrian on Footpath Figure 4b: Vendor Encroachment 7 Figure 4c: Pedestrian Crossing Point

14 Application of Global Walkability Index (GWI): Case Study Bangalore, India 5.3 Sampige Road, Malleshwaram Sampige Road is a commercial street in the heart of the old neighborhood of Malleshwaram in Bangalore. The sidewalks are broad but encroached upon by vendors. Crossing the street is a problem amidst cars and two wheelers that do not yield to pedestrians. Furthermore amenities like benches or places to rest are missing. Trees provide comfortable shaded environment to walk under but also are a major obstruction on the footpath. Table 5: Walkability Survey, Sampige Road, Malleshwaram Criteria Remark Score Walking Path Modal Conflict Availability of Walking Paths Availability of Crossings Grade Crossing Safety Motorist Behaviour Amenities Disability Infrastructure Obstructions Security from Crime Walking Environment Segregated footpath Conflict with vendors 3.5 m wide on both sides (see Figure 5a) Encroachment by shops and vendors (see Figure 5b) No defined crossing except at junctions (100m) Road markings exist but no pedestrian priority (see Figure 5c) Low as cars have priority and generally do not slow down Transformers installed at junctions Do not yield to pedestrians Long waiting time Physical separation from main road Well shaded with trees Street lights at regular intervals No seats, public toilets or dust bins Footpath heights are not negotiable by wheelchair Plenty of obstructions and walking surface not uniform Encroachments from shops and vendors Existing physical barriers like lamp posts and trees Obstruction by animals Vibrant mixed use Improvement in lighting is needed Threat of pick pocketing Noise level at 85dB Air pollution levels high Total 21/50 8

15 Application of Global Walkability Index (GWI): Case Study Bangalore, India Figure 5a: Pedestrian on Sidewalk Figure 5b: Vendors on Footpath Figure 5a: Pedestrians Crossing at Junction 5.4 Gandhi Bazaar Road, Baswangudi Gandhi Bazaar is a busy commercial street located in the old neighbourhood of Baswangudi. Sidewalks are present but encroached making it impossible to walk on them. Pedestrians are forced to walk with motorised traffic. Crossing the street is Table 6: Gandhi Bazaar Road, Baswangudi difficult as vehicles are fast moving and do not yield. Furthermore, pedestrian infrastructure like benches, water points and dustbins are absent. Cows are also spotted near flower and vegetable shops and further block smooth passage. Trees provide shade while walking in the daytime. However they also encroach most of the sidewalk. Criteria Remark Score Walking Path Modal Conflict Availability of Walking Paths Availability of Crossings Grade Crossing Safety Pedestrian pathways exist but people end up walking on road due to encroachment 3.2 m wide on both sides Encroached by 2-wheeler parking, local vendors and shop encroachments. Trees and lamp posts also obstruct walking No defined crossing except at junctions (200m). People jaywalk to cross Traffic is not controlled hence pedestrian has to negotiate the road himself Motorist Behaviour Amenities Disability Infrastructure Pedestrian has to wait for the time when fewer vehicles are on the road and then cross Physical separation from main road Street lights No seats or public toilets Footpath heights are not negotiable by wheelchair Plenty of obstructions Obstructions Encroachments from shops

16 Application of Global Walkability Index (GWI): Case Study Bangalore, India Existing physical barriers like lamp posts and trees Security from Crime Walking Environment Vibrant mixed use Improvement in lighting is needed Noise level at 80 db Air pollution levels high 3 2 Total 19/50 Figure 6a: Pedestrian on Main Road 6.0 ANALYSIS Tabulating the walkability score for all four streets reveals that overall Bangalore streets score low on the GWI. The average score for the city stands at 20.25/50. On the other hand, Walkscores for the same streets generate high scores (see Table 7). This is on account of the fact that all streets under study had a healthy mixed use environment. An average score of 91.5/100 is indicative of this. This further illustrates that GWI is a more appropriate tool is assessing street environment in the Indian context. Low walkability scores are primarily attributed to four factors: 1 Lack of adequate infrastructure or poor design of the walking environment making 9th MainJayanag ar Walkability (50) Modal Conflict Table 7: Walkability Index: Bangalore, India BrigadeRo ad Figure 6b: Vendor Encroachment it difficult for people to enjoy their walking experience. 2 Weak policy and law that does not cater to pedestrians 3 Weak implementation of footpath encroachment regulations, traffic management and pollution standards 4 Lack of education and awareness on part of pedestrians and motorists in adhering to basic road rules and regulations. This analysis advises future attempts at improving the walking environment by suggesting that measures must not be restricted to mere infrastructure improvements. A well rounded street improvement project must take into account all these four factors for a larger and sustained impact on improving walkability. Sampige RoadMalleshwar am Figure 6c: Average Height of Footpath Gandhi BazaarBaswang udi AverageSco re Path

17 Application of Global Walkability Index (GWI): Case Study Bangalore, India 9th MainJayanag ar BrigadeRo ad Sampige RoadMalleshwar am Gandhi BazaarBaswang udi AverageSco re Availability Crossing Availability Crossing Safety Motorist Behaviour Amenities Disability Infrastructu re Obstruction s Security from Crime Walking Environmen t Average City Score Walkscore(100) CONCLUSION Recent global developments have shown that investment in non-motorized modes have a positive impact on a city s mobility. Cities like Amsterdam and Copenhagen are living proof of such efforts. In this light, there is an urgent need in Indian cities to take a closer look at the pedestrian environment and work towards improving the safety and comfort standards for this. In this context GWI is a comprehensive tool that can be adopted to rate streets, especially in the Indian context. Adding the 10th criteria for Environment Quality can further help elaborate this method. Availability in the form of a smart phone app is a great initiative that makes it easy to use and generate street scores. It can further encourage citizens to engage in reporting scores for their local streets. Also the availability of scores on an online open source platform will help researchers access and use this information. Analyzingresults, based on the cause of low scores, aims at assisting municipalities to device better and well rounded policies and projects to improve walkability. REFERENCES 1 TERI. (2010). Air Quality Assessment, Emission Inventory and Aource Apportionment Study for Bangalore City. TERI, New Delhi. 2 Ministry of Urban Development. (2008). Traffic & Transportation Policies and Strategies in Urban Areas in India. Ministry of Urban Development, India 3 Indian Institute of Human Settlements. (2011). Urban India 2011: Evidence. Autumn Worldwide. 4 Abley, S. (2005).Walkability Scoping Paper. [accessed 15 April, 2014] 11

18 Application of Global Walkability Index (GWI): Case Study Bangalore, India 5 Krembeck, H. & Shah, J. (2006). The Global Walkability Index. Massachasetts Institute of Technology, MIT Libraries 6 Leather, J. (2011).Walkability and Pedestrian Facilities in Asian Cities. Asian Development Bank, Philppines\ 7 RITES India. (2011). Comprehensive Traffic and Transportation Plan for Bengaluru. Karnataka Urban Infrastructure Development and Finance Corporation. 12

19 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 EVENT DAY EFFECT ON PEDESTRIAN CHARACTERISTICS FOR CBD STREET OF INDIAN METROPOLITAN CITY Hardik S Sukhadia*, Sanjay M Dave**, Jiten Shah #, Dipak Rathva ## Abstract: Walking is one of the most important, economical, flexible and eco-friendly mode of transportation. In India, walking on carriageway instead of sidewalk is widespread scenario reasons being sidewalks are inadequate, encroached by hawkers, vendors and unprotected with guard rail. The situation becomes worst on CBD streets during event occasion and weekends peak hours. This study is aimed on pedestrian flow behaviour on normal week day and event day. The study was carried out by video recording of pedestrian movement in CBD area of Vadodara city for one normal and two event days. The analysis helps in accessing the behaviour, volume, speed, density, space and level of service of pedestrian flow in both conditions. Pedestrian flow on normal working days was laminar one with somewhat queuing pattern observed for most of the survey period with average rate of flow 10 pedestrian/min and maximum rate of flow 22 pedestrian/min during peak hours. On other hand platoon effect was observed for religious event day of Navratri Durgashtami (Event day-1) with average rate of flow 22 Pedestrian/min and maximum rate of flow 45 Pedestrian/min during peak hours. The average pedestrian speed calculation worked out for study stretch was observed m/sec for normal working day and for the religious event day observed m/sec. The comparative pedestrian characteristics for two conditions revealed that some TSM action to improve pedestrian movement is necessary on event day. Keywords: Pedestrian Characteristic, walking speed, CBD, Event day, Normal working day 1.0 INTRODUCTION Waking is most reliable, sustainable, health gainer activity. Also the walking is one of the cheapest modes of transportation with highly contributing safe and liveable environment. Every journey starts and ends with walking trips. In developing country at least 40% journeys up to 1-2 km are walking trip. (Source: ADB Bank and HCM 2000). In such countries different areas of the metropolitan and 2 nd order metropolitan cities share large number ofwalking trips. Among these areas CBD areas generates majority walking trips. Sidewalks have been placed both sides of the carriageway for easy and safe movement of pedestrian mobility without interacting to vehicle traffic. In developing country like India the vehicle growth rate ishigh in last decade. Also urbanization rate of metropolitan cities is high in last decades. This resulted in more importance to planning of facilities for vehicular movement and negligence towards pedestrian environment. In order to achieve high speed and rapid connectivity for vehicular traffic, there is stress for the expansion of carriageway width. In typical CBD areas of historical cities of India, where old planned city roads suffers from lack of space is main problem. This trend leads the curtailment of the sidewalk. In addition, the problem encroachment due to hawkers and vendors reduce the effective walkway width of the sidewalk. This compels to pedestrian walk on the carriageway. The situation becomes very chaotic to control on event day such as religious festival days. On event day in Indian condition hawkers and vendors encroach near to the religious place and also the flow of pedestrian movement increases. Sometimes flow of pedestrian shifts to the carriageway. The pedestrian flow on the carriageway creates friction with vehicular traffic. Due to this interaction between vehicle and pedestrian there are chances of accident or the problem ends with the traffic jam. This problem of * PG Student, Civil Engineering Department, The M.S.University of Baroda, sukhadia_h@yahoo.com **Associate Professor, Civil Engineering Department, The M.S.University of Baroda, smdave@ymail.com # PhD Scholar, Civil Engineering Department, SardarVallabhbhai National Institute of Technology, Surat, jitenshah_civil@yahoo.co.in ##Assistant Professor, Civil Engineering Department, The M.S.University of Baroda, dipak_rathva@yahoo.co.in

20 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City pedestrian requires some Transport System Management (TSM) action because such event based pedestrian flow occurs very frequently in India. Also in India, very little attention has been paid to study pedestrian behaviour; particularly in CBD area hence this study will provide some base for investigating pedestrian behaviour under influence of religious and festival event and also help local authority to frame appropriate measures for safe and comfortable movement of pedestrian. 2.0 LITERATURE REVIEW Very few research studies have been reported on pedestrian characteristics in India. Literature on pedestrian studies is quite diverse to the vehicular traffic. The pedestrian studies conducted abroad include pedestrian flow characteristics and modelling of flow parameters. Many researchers have examined the influencing factors of pedestrian characteristics. Some of the researchers Fruin (1971), Polus et al. (1983), Tarawnch (2001), Montufar et al. (2007), and Finnis and Walton (2008) observed that walking speed of female pedestrian is slower than male pedestrian and walking speed reduces with age of pedestrian. The speed of pedestrian is also affected by type of pedestrian (i.e. local or outsider) Tanabariboon et al. (1986) observed that the mean walking speed of the Singaporeans is slower (74m/min) in comparison with speed of U.S. pedestrians (89m/min). Polus et al. (1983) found that the average walking speed for pedestrian of Israel is 79 m/min, while Khoushki (1988) found 65 m/min in Riyadh and Morrall et al. (1911) found the walking speed 75 m/min in Colombo, Sri Lanka respectively. RajatRastogi (2010) found the average walking speed 72 m/min for Indian pedestrian. Tarawnch (2001), Carey (2005), and K. Singh and P.K.Jain (2011) found that group size affect the walking speed significantly. P.K.Jain (2011) concluded that effect of group size on walking speed is low for group size up to 3 and high for group of five or more. Carey (2005), Montufar et al. (2007), K.K.Finnis and Walton (2007), and Jianhong (2012) found that younger pedestrian are faster than older and children. Dammen and Hoggendom (2005) observed that pedestrian walking speed depends on walkway characteristics such as width, type of facility (i.e. with or without guardrail) and environmental factor. The walking speed of pedestrian is also affected due to activity performed during walking. Morrall et al. (1991), K.K. Finnis and Walton (2007), Ronald Galizo and Luis Ferriro (2012), Jianhongchen and Nanjing jian (2012), Kotkar Kishor et al. (2010) found that pedestrian walking with luggage are slower than those pedestrian who has no luggage. Young (1998) found that walking speed of the pedestrian is significantly differing from those wearing headphones and talking on cell phones. The land use pattern is also been subject of research. Al-masuied et al. (1993), K. Singh and P.K. Jain (2011) found that the walking speed is differing with different land use such business area, residential area, educational area. They also found that surrounding environment is an important factor which affects the walking speed of pedestrian. Lam and Chang (2000) observed that pedestrians walking in commercial areas are faster than those in residential areas. Finnis and Walton (2007) found the walking speed of pedestrian in indoor walkways is slower than outdoor walkways. K.K. Finnis and Walton (2007) observed that commuters have significantly higher walking speed than others. He also observed that pedestrian talking to other and observing surrounds has slower speed than commuters. He also found that the pedestrian who wears flip-flop shoes are slower than others. He also found that there was no effect of gradient on walking speed up to 0 o to 4 o. However, the walking speed significantly reduces at gradient more than 5 o. 14

21 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City Al-Masaeid et al. (1993) developed the pedestrian speed flow relationship for the central business district (CBD) areas in Irbid, Jordan. Quadratic polynomial regression relation was found to be the best fit. They observed that in the design of CBD sidewalks, pedestrian demand to capacity ratio should be limited to 0.5. Pedestrian flow was analyzed on the basis of effective width of sidewalk rather than the lane concept. Many researchers have studied pedestrian flows accross the country as well as abroad under different conditions like outdoor walkway, sidewalks in central business district (CBD) areas, under mixed traffic conditions, prevailing pedestrian flow unidirectionally and bidirectionally. They have devoloped flow speed relationship. Tanaboriboon et al. (1986) developed flow relationships for sidewalks and walkways in Singapore and compared them with those obtained for the United States and the Britain.The relation between speed and density becomes exponential under heavy pedestrian flow. Polus et al. (1983) developed single and three regime linear speed-density models for pedestrian flows on sidewalks in CBD of Haifa (Israel).In similar type study Al- Masaeid et al. (1993) found that the quadratic polynomial relation fits the speed-flow data the best relationship between speed and density was found to be linear while flowdensity and flow-speed relationships were quadratic. 3.0 OBJECTIVES AND SCOPE OF THE STUDY 1. To study the pedestrian movement characteristics on normal working and event day. 2. To evaluate Level of service for the study area. 3. To recommend the appropriate TSM action for smooth movement of pedestrian due to event effect in study area. 4. The scope of this study is limited to the typical CBD street attracting considerable pedestrian trips on normal working day and substantial rise of the same on event days. 4.0 STUDY AREA Vadodara is third largest city of Gujarat and eighteenth largest city of India. It is situated on banks of river Vishwamitri. It has an area of km 2 and urban population of 1.8 million (Census 2011). It is known as cultural city and it has reputation as educational hub as well as chemical hub of Gujarat. Vadodara has 1680 km paved and 400 km of unpaved roads.average trip length for city is about 6 km, due to which mode share is inclined towards private vehicles comprising about 50% of total modal split. Public transport is one of the areas, which has been lacking far behind in the city. Inadequacy of public transportation system restricts its mode share to 6% while 15% of trips are taken care by IPT (autos) and 29% by non-motorised transportation (NMT). The percentage share of walking trip is found to be 66% of total NMT, particularly in CBD areas (RITES 2006). The selected study stretch is located in one of the major arterial corridor of the oldest CBD area covered with four gates called as a walled city (Figure 3). The study stretch was selected in such a way that it comprises mix land use like commercial activity at ground level and residential at first floor level and above as well as having good number of religious places resembling to typical Indian cities. The main attraction toward wall city is historical temples, jewellery shops and cloth market, which generate huge amount of pedestrian movement. The sidewalk is only the facility, which provides pedestrian movement and is generally encroached by hawkers and vendors. This is common features of the study stretch and this activity increases during event day resulting in reduction of effective walkway width. Due to reduction in space, pedestrian have restricted choice for movement and to gain walkable speed they share the main carriageway of vehicular flow. This hinders vehicular traffic and highly 15

22 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City affects their safety too. Especially in event day pedestrian movement is quite high compared to normal working day which requires to be addressed considering safety and smooth flow of pedestrian. This study mainly focuses on the comparison of pedestrian movement on normal working day and event day. 5.0 DATA COLLECTION AND METHODOLOGY The study stretch included typical CBD Street having high commercial activities and religious temples. A study was carried out for three different days and locations on the same street. These included a normal working day and two event days of Durgashtami (Event day-1) and Dushera (Event day-2) of Navratri which is predominant festival. The selection of study stretch has been done in such a way that it includes commercial and residential land use on both sides of the road, freedom from encroachment by hawkers and vendors and at least 15 meter away from cross road to have fair assessment of pedestrian characteristics. The detailed cross section of study location is shown in Figure 1. Video-graphic technique was employed for collecting the pedestrian data. A strip of known length (6 meter) was marked on the sidewalk by using a white or yellow oil paint depending on floor material for measurement of walking speed and flow of the pedestrian. The pictorial view of study stretch with trap marking is shown in Figure 2. Figure 1: Cross Section of the Study Stretch on M.G. Road Figure 2: Strip Marked on Sidewalk A high mega pixel (14.0 MP) video camera was used for accurate data collection, which was installed at an elevated point so that it is possible to covers the pedestrian movement on the entire strip of sidewalk as well as cover the half of the carriageway for the pedestrian flow on carriageway. The pedestrian movement was recorded during 10:00 to 17:00 hrs for the normal and event days. The required pedestrian data were later extracted from the 16

23 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City recorded videos by using video player operating at normal speed. The basic parameters of pedestrian flow data were extracted on one minute basis through observing entry and exit movement of pedestrians in the marked strip length. The characteristics of surrounding area were such that pedestrian could not walk on the dedicated path due to encroachment by hawkers and shopkeepers to display their product, resulting in pedestrian spill over on the main carriageway. Pedestrian observation was carried out on sidewalk and carriageway considering directional movement, gender, age, and carrying of luggage/children. For pedestrian flow, the number of pedestrian passing the marked line was noted per minute basis. The time taken by pedestrian to cross the strip length was noted with least count of 0.01s stopwatch to determine pedestrian speed. At least five pedestrian of each category (age, gender and luggage or without luggage) were selected for speed analysis per minute. Samples for the analysis of data were collected from total 420 minute of videographic survey of each day. Based on the pedestrian flow, pedestrian density per square meter was calculated by pausing video at interval of every 3 second. Figure 4: Pedestrian Movement on Sidewalk 6.0 RESULT AND ANALYSIS The classified data of 38,108pedestrians was extracted from video considering movement on sidewalk and carriage way i.e. closed to parking. It was observed that composition of pedestrian on sidewalk and carriageway is dominated by female pedestrian with about 65% share of total pedestrians on normal working day as well as on event day. From the result it is observed that movement of pedestrian is quite high in event days due to the peculiarity of the CBD area such as major whole sale and retail cloths, jewellery markets as well as some religious place (Figure 4). It is also observed that about 20% and 33% of total volume of pedestrian walking on carriageway on normal working and event day respectively (Figure 5) Figure 3: Study area location 17

24 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City Figure 5a: Pedestrian volume on half hourly basis for sidewalk and carriageway Figure 5b: Pedestrian volume on half hourly basis for sidewalk and carriageway Figure 5c: Pedestrian volume on half hourly basis for sidewalk and carriageway Figure 5 shows significant movement of pedestrian on carriage way and this is due to encroachment and other activities on sidewalk, resulting in reduction in effective walkway width. Also those pedestrian who want continuous and uninterrupted walk are unwillingly forced to share the carriage way and their volume reaches to 40% to 50% of the total volume, particularly on event day. Such trends shows pedestrian put their life and other vehicular flow in risk. It indicates that there is an urgent need to improve the pedestrian environment on the sidewalk for better performance of pedestrian activities as well as smooth vehicular flow. The study also examines the composition of pedestrian characteristics based on age, gender, luggage carrying pedestrian and directional movement on sidewalk. From the data it is found that during normal working day from total (4,567) pedestrian movement in downstream (2,462) is higher than the upstream (2,105), during event day-1 from total (8,433) pedestrian movement in upstream (4,640) is higher than the downstream (3,793) and during event day-2 from total (10,075) pedestrian movement in downstream (52,42) is higher than the upstream (4833). Figure 6 shows the gender wise composition of pedestrian movement in both 18

25 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City the direction. Figure 6 illustrate that the ascending pedestrian flow constitutes 34% male and 66% female where as in downstream direction 35% male and 65% female are observed. The composition of female pedestrian was dominated by about 65% of total pedestrian volume on normal working as well as on event day. Pedestrian were grouped into three categories as children (< 15 years), young (15-50 years) and elder (> 50 years). The proportion of children, younger and elder is 10%, 84% and 6% respectively in both the directions. Figure 6: Gender wise pedestrian composition for normal day Figure 6b: Gender wise pedestrian composition for event day-1 Figure 6c: Gender wise pedestrian composition for event day-2 19

26 Sidewalk Downstream Upstream Sidewalk Down Stream Up Stream Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City It was also found 2-4% of total pedestrian was found walking with luggage in both the directions. The walking speed is subjected to pedestrian characteristics such as gender, age, walking with luggage and direction of movement. From the analysis of speeds of all selected pedestrians, the average walking speed on sidewalk was 1.0 m/s on normal working day and 0.91 m/s on event-1 and 0.9 m/s on event-2. This reduction in speed of pedestrian on event day may be accounted for their wandering behaviour on display of products and attractive offers by the shopkeepers who encroaches the side walk also. It has also a side effect on the pedestrian who follow them. Table 1 provide the statistical analysis of mean walking speed for various conditions. Facility Gender Male Table 1a: Statistical analysis of mean walking speed for normal working day Female Male Female AGE Loading Condition Sample size Mean (m/s) Median S. D Variance CV W/O Luggage 1, With Luggage W/O Luggage 2, With Luggage W/O Luggage 1, With Luggage W/O Luggage 1, With Luggage Children Young --- 5, Elder Facility Table 1b: Statistical analysis of mean walking speed for Event-1 day (Durgashtami) Gender Loading Sample Mean Condition size (m/s) Median S. D Variance CV Male W/O Luggage 1, With Luggage Female W/O Luggage 2, With Luggage Male W/O Luggage 1, With Luggage W/O Luggage 2, Female With Luggage AGE Children Young --- 7, Elder

27 Sidewalk Down Stream Up Stream Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City Facility Table 1c: Statistical analysis of mean walking speed for Event day -2 (Dushera) Gender Male Female Male Female AGE Loading Sample Condition size Mean Median S. D Variance CV W/O Luggage 1, With Luggage W/O Luggage 3, With Luggage W/O Luggage 1, With Luggage W/O Luggage 3, With Luggage Children --- 1, Young --- 8, Elder Table 1 (a, b, c) shows that in both, normal as well as event days, walking speed on upstream is found lower (1.01m/s) than the downstream (1.2m/s). The results reveals that average walking speed is quite lower compared to the speeds reported in literature RajatRastogi et.al (2010). The mean speed of pedestrian carrying luggage for all the categories: upstream and downstream in both normal as well as event day was lower than the without luggage carrying pedestrian except event 2, female walking in downstream. The deviation in mean speed was higher in pedestrian with luggage on normal working day. This might be due to small sample size having affected by higher percentage of without luggage carrying pedestrian that means they must follow the speed of stream flow contributing higher percentage of pedestrian without luggage. For that reason there was close difference in average walking speed of without and with luggage pedestrian except for females moving in upstream direction. Whereas in event days, deviation and coefficient of variance of pedestrian speed is lower in luggage carrying condition with exception of event day 1 downstream pedestrian flow. This may be due to volume of pedestrian moving with luggage was quite higher than the normal day and luggage condition may restrict their speed to some extent. Table 1(a, b, c) proves that there is significant effect of luggage on the walking speed. The higher walking sped is observed by younger pedestrian followed by children and elder. The interesting finding for all cases is; average waling speed of elder pedestrian is almost half of the children. On normal working day, pedestrian movement gradually increases up to evening with peak observed evening as shown in Figure 5. Whereas pedestrian rush found to be constant throughout the day time with one peak in afternoon in both event days. The same variation is reflected in term of density as shown in Figure 7. As volume increases density of pedestrian also increases. 21

28 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City Figure 7a: Time series plot for Density variation (Normal day) Figure 7b: Time series plot for Density variation (Event day-1) Figure 7c: Time series plot for Density variation (Event day-2) Figure 7 shows increase in density with time and generally density represent the congestion level. As the density increases, it is difficult to find the space for the further movement of pedestrian and it restricts the speed. In both event days, large pedestrian movement was found with relatively higher percentage of luggage carrying pedestrian which reduced the service level of pedestrian. The level of service (LOS) describes the comfort level of pedestrian. It is observed that average pedestrian density is three times on event day-1 and event day-2 (0.3 ped/m 2 ) than the pedestrian density on normal working day (0.1 ped/m 2 ), which reflects deterioration in the LOS for the pedestrian movement. Table 2 and Table 3 shows the current Level of Service for existing facility of study area. 22

29 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City Pedestrian Flow In Normal working day (p/m/min) Table 2: Volume based LOS for existing facility of study area LOS As per (HCM 2010) In Event day Day-1 (p/m/min) Day-2 (p/m/min) Avg. flow on event days (p/m/min) LOS As per (HCM 2010) % Change Normal and Event day Maximum 22 D D 41% Average 16 C D 50% From Table 2 it is seen that the level of service evaluated on basis of volume concentrates is around C for normal working day and it declines up to D for event day. It indicates that some measures are required for the smooth pedestrian movement by some TSM action like prohibition of on street parking, hawkers and roadside vendors particularly on event days. Pedestrian space is characterized as reciprocal of density. Considering area module and volume, the LOS for normal working day was observed to be D but on event days it declined up to E considering the area module as shown in Table Flow rate 3 and which reveals that the sidewalk functions with lower level of performance. This may be due to the higher percentage of the luggage carrying pedestrian. Noteworthy that the study results does not included the movement of pedestrian who were walking on carriage way on event and normal days. If included, further declination of LOS up to E or F may have been observed. The study results give an idea about the pedestrian flow characteristics on both normal as well as various event days and pointed out the need of TSM action for better performance of side walk. Table 3: Comparative Level of service based on space (m2/p) Normal working day Level of Service Day-1 Event days Day-2 Average of Event day-1 and 2 Level of Service Maximum 3.70 D E Average B D 7.0 CONCLUSION The study carried out in CBD area of Vadodara city revealed that pedestrian have to endanger their safety for mobility on the event day. From the study, it was found that the pedestrian volume in study area increased up to 90% on the event day as compare to normal day. The significant bi-directional movement observed in study area induces friction and restricts comfortable movement within the sidewalk. As compared to normal working day the mean walking speed decreased up to 10%, density increased 80 to 100% and space decreased 70 to 100% on event days. The study also found that almost 40 to 50% of total pedestrian walk on carriageway which may cause obstruction to the smooth vehicular traffic flow. The comparative study regarding pedestrian flow, speed, density and space revealed that pedestrian environment degrades to significant extent on the event days. Considering the fair number of religious event and festival occurring during a year, a permanent strategy to improve the safety and smooth flow of pedestrian is necessary in Indian context. It is proposed to prohibit on street parking on event days and implement effective enforcement 23

30 Event Day Effect on Pedestrian Characteristics for CBD Street of Indian Metropolitan City measures to prevent the encroachment of sidewalk. This TSM action may be improve the pedestrian environment in CBD area. REFERENCE 1. Al-Masaeid, H.R., Al-Suleiman, T.I. and Nelson, D.C., (1993). Pedestrian Speed Flow Relationship for Central Business Areas in Developing Countries. Transportation Research Record 1396, National Research Council, Washington, Dammen, W., and Hoogendoorn, S. P. (2003). Experimental research of pedestrian walking behaviour, Proceedings of Annual Meeting of Transportation Research Board, CD-ROM, National Academy Press, Washington.Guidelines for pedestrian, Indian Road Congress-103, Finnis K.K., Walton D., (2007). Field observations of factors influencing pedestrian walking speed. 4. Galiza R., Ferreira L., (2012). A methodology for determining equivalent factors in heterogeneous pedestrian flows. 5. Highway Capacity Mannual (2010). 6. Jianhong Y., Xiaohong C., Nanjing J., (2012). Impact analysis of human factors on pedestrian traffic characteristics. ELSVIER, The fire safety journal. 7. Kotkar K. L., Rastogi R. and Chandra S. (2010). Pedestrian Flow Characteristics in mixed flow condition, ASCE, Journal of Urban Planning & Development,136(3) Koushki, P. A. (1988). Walking characteristics in Central Riyadh, Saudi Arabia. Journal of Transportation Engineering, 114(6), Larusdottir A. R. and Dederichs A. S., (2010). Evacuation of children: movement on stairs and on horizontal plane. Journal of Fire Technology. 10. Leather J., Fabian H., Gota S., and Mejia A., (2011). Walkability and pedestrian facilities in Asian cities. ADB Sustainable Development Working Paper Series. 11. Montufar M., Arango, J., Porter, M., and Nakagawa, S. (2007). Pedestrians normal walking speed and speed when crossing a street. Transportation Research Record, Transportation Research Board, Washington, DC, Parviz A.K., (1988). Walking characteristics in central Riyadh, Saudi Arabia. ASCE, Jour-nal of Transportation Engineering. 13. Polus, A., Schofer, J. L. and Ushpiz, A. (1983). Pedestrian flow and level of service, ASCE, Journal of Transportation Engineering, 109(1), Rastogi R., Thaniarasu I., Chandra S., (2011). Design implications of walking speed for pedestrianfacilities. ASCE, Journal of transportation engineering. 15. Singh K., Jain P.K, (2011). Methods of assessing pedestrian level of service. Journal of Engineering Research and Studies. 16. Tanaboriboon Y., Hwa S. S, and Chor C. H., (1985) Pedestrian characteristics study In Singapore. ASCE, Journal of Transportation Engg. 17. Young, S. B. (1998). Evaluation of pedestrian walking speeds in airport terminals. Transportation Research Record 1674, Transportation Research Board, Washington, DC,

31 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 EFFECT OF LANE FRICTION ON SPEED OF NON-MOTORIZED VEHICLES Prasham Khadaiya* and Rajat Rastogi** Abstract: Traffic flow on roads consists of three components, i.e. motorized vehicle flows, non-motorized vehicle (NMV) flows and pedestrian flow. This paper concentrates on the NMV (bicycle and cycle rickshaw together) flows on urban roads. The study was carried out in Roorkee city, a small sized city with high intercity and local traffic on its roads. Data were collected through a combination of manual and videography methods on three roads, all of two-lanes with either one-directional or two-direction traffic. The impacts of roadway space and flow constraints and traffic composition and flow on speed of NMVs were studied. The analysis indicated that on a road segment with no friction from opposite direction, the NMV speed is reduced with an increase in total flow; on segment with friction from opposite direction it is reduced with increase in opposing direction flows, while on segment with all frictions it is reduced with NMV, total and opposing direction flows. The gap between speed ranges of NMVs and MVs also reduced under the three scenarios. The percent share of NMVs in total flow also influenced the speed profiles. Keywords: Non-motorized Vehicles, Speed, Lane Frictions, Composition, Flows 1.0 INTRODUCTION The non-motorized vehicles (NMVs) are those vehicles which require human power for their mobility. NMVs offer low cost transport, are environment friendly (no pollution), use renewable energy, and emphasize on the use of labour rather than capital for mobility. These are well suited for short trips in most cities regardless of income and offer an alternative to motorized transport especially under traffic congestion conditions. These can be considered as an appropriate element in strategies dealing with poverty alleviation, air pollution, management of traffic problems and motorization, and the social and economic dimensions of structural adjustment. These play a complementary role to public transportation. Varieties of non-motorized users are present on roads in different countries and their share is reported to be continually increasing over years. Each of the vehicle type has different flow characteristics. With the increasing variety of emerging NMV users comes the question whether we are designing and building suitable facilities for them? Many jurisdictions throughout the United States have adopted the American Association of State Highway and Transportation Officials (AASHTO, 1999) Guide for the design, layout and development of bicycle facilities. In India, IRC outlines the recommended practice for the design and layout of cycle tracks. This guide is written with bicyclists in mind and is based on old research. This paper focuses on the possibilities of contributing to the existing code of practice and incorporates the bicyclists and cycle rickshaws for their study. In the process, the impact of flow composition, operational characteristics of roads and flow and space frictions on speed of cycle, cycle rickshaw and both combined as NMVs is examined and studied. Not much work is reported in literature regarding the flow analysis of NMVs in conditions similar to that in India. Navin (1994) experimentally determined the operating performance of a single bicycle and traditional traffic flow characteristics of a stream of bicycles. These were compared with the observed data. Wang and Wu (2003) examined the characteristics of motorized, * Post Graduate Student, Transportation Engineering Group, Dept. of Civil Engineering, Indian Institute of Technology (IIT) Roorkee, prasham592@gmail.com ** Associate Professor, Transportation Engineering Group, Dept. of Civil Engineering, IIT Roorkee, rajatfce@iitr.ac.in

32 Effect of Lane Friction on Speed of Non-Motorized Vehicles non-motorized and pedestrians individually and as well as the interference among them in mixed traffic conditions. Duthie et al. (2010) examined the impact of design element including the type and width of the bicycle facility, the presence of adjacent motor vehicle traffic, parking turnover rate, land use and the type of motorist-bicyclist interaction. Hongwei et al. (2011) found that when a non-motorized lane is provided adjacent to the curb parking space, the effective lane width of the nonmotorized lane decreases. El-Geneidy et al. (2013) examined travel speed of bicyclists on various types of facilities, namely on-street, off-street, and mixed traffic. In India IRC: provide the guide lines for cycle tracks such as need of cycle track, capacity, horizontal curves, vertical curves, sight distance, lane width, vertical clearance, horizontal clearance, etc. 2.0 DATA COLLECTION 2.1 Study Area Roorkee was selected as the study area to conduct the study related to NMV flows. The city has a semi urban-rural setting with lots of daily trips being made to the city from adjoining villages. The city is one of the entry points to the Uttarakhand state and caters to the traffic coming from Muzaffarnagar side and going to Haridwar, Dehradun and beyond. It is on NH-58 and NH-73. The city has high proportion of NMVs in the total flow. 2.2 Location Characteristics To select a suitable location for carrying out NMV study, following general points were given consideration: NMVs share in the total traffic is high Effective width of the road remains uniform along the segment considered No intersection falls within the segment and adjacent it which can influence the speeds The road segment is clearly visible from an accessible vantage point Apart from the above considerations, following flow considerations were also considered: Number of lanes were fixed to two with adequate shoulders on sides The possibility of segregation of traffic by way of provision of a median Possible variation in the flow and its composition Considering the above points, three data collection locations were identified. These were: a) Segment between MalviyaChowkand BSM Y-intersection on NH-73 b) Civil lines Road near petrol pump intersection c) Rampur Road (Old NH road) These locations are referred as Location-1, Location-2, and Location-3 respectively in rest of the paper. The physical features of the road segments are given in Table 1. 26

33 Effect of Lane Friction on Speed of Non-Motorized Vehicles Table 1: Physical features of the selected road segments Data Number Width of Roadway Flow direction Friction level collection location of lanes carriageway (m) width (m) Location * 8.20* One-directional Median on one side, negligible influence of parking of vehicles Location Two-directional Opposing traffic, higher influence of parking of vehicles Location Two-directional Opposing traffic, high influence of parking of vehicles and shops on sides This is half of the divided carriageway / roadway. The road is 4-lane divided. The level of friction as mentioned in the table indicates that the effective width available to the vehicles is lower than what is given. Location 2 and 3 have some open space in front of the shops but that was consumed mainly by the shops. Therefore, the level of friction has increased as we move from location 1 to location 3.The three locations are shown in Figure 1. a) Location 1 b) Location 2 c) Location Method of Data Collection Video graphic method was used to capture the traffic flow at the selected locations. The camera was fixed at an elevated position, usually at the top of an adjoining building, to obtain an overall view of the selected test location. The data were collected in the Figure 1: Vehicular movement at different locations morning and evening hours when the flow was high. The physical features of the test location were measured using a 30 m tape. Speed of the vehicle was estimated with respect to a trap length being marked on the carriageway. The details of the data collection effort at different locations are given in Table 2. 27

34 Effect of Lane Friction on Speed of Non-Motorized Vehicles Table 2: Details of data collection at different locations Data collection Time period Duration (minutes) Length of segment (m) location Location 1 8:30 am to 10:40 am Location 2 Location am to am 4.00 pm to 5.30 pm am to am 5.00 pm to 6.00 pm Data Processing Data were processed in the office by playing video graph on the monitor. The flow characteristics of the cycle, cycle rickshaw and other motorized vehicles were measured separately considering a two minutes interval using system stop watch. Data were recorded in the MS-Excel work sheet for further processing. Based on the requirements the time intervals of successive two minutes were converted into four minutes interval. Flow of motorized and non-motorized vehicles was extracted in every 2-min time interval and this was converted to equivalent hourly flow units by using appropriate multiplier. Two white lines were marked on the carriage way at distances as mentioned in Table 2. The time taken by a vehicle in traversing this distance was noted using system stop watch. Thus, the speeds of the vehicles were estimated 3.0 NMV FLOW- CHARACTERISTICS 2.5 Traffic Composition The traffic flow composition at different locations is given in Figure 2. (a) Location 1 (b) Location b (c) Location 3 Figure 2: Composition of traffic at three locations The specific nature of the road section can be studied from the traffic composition as shown above. First location shows almost all categories of the traffic which is true as this falls along NH-73. Second location is shopping area with open spaces and restriction on movement of heavy vehicles and commercial vehicles. Cars are also allowed for lesser time periods. This is clear from only 3% share of cars and nil presence of heavy and commercial vehicles. The third location is in old city which is densely occupied. This location has shops on both the sides which occupy spaces on the road side thus pushing the vehicles to be parked to the shoulders. Reduction in effective space has resulted in absence of big size vehicles at this location. The share of NMVs on the three locations is 33%, 46% and 51% respectively. Within the NMVs, the share of cycle rickshaw is 18% at first location and 41% at rest of the two locations. The location 2 and 3 are shopping areas and hence is the reason of increase in the share of cycle rickshaws. 28

35 Effect of Lane Friction on Speed of Non-Motorized Vehicles 2.6 Space Occupied at Road Side Observations were taken regarding the space being occupied by NMVs and bicycle and cycle rickshaw individually from the edge of the carriageway. This would provide an initial account of the space that need to be marked on roads for the exclusive usage of NMV modes. Figure 3 presents the overall space occupied by the NMV category at the three locations. It can be observed that majority of NMVs are moving within 2m from the edge of the carriageway on location 1 and on one side of the location 2. Side friction in terms of parking of vehicles has caused low usage of space within 1 m from the edge of the carriageway. Parking of bigger vehicles has shifted the NMV flow towards the centre of the carriageway. Location 3 plot clearly show the impact of space being occupied by the shops and the parking of vehicles on road side in terms of the higher usage of middle space on the carriageway. This is between 2 to 3 m on side and from 1 to 4 m on the other side of the carriageway. Location 1 Location 2 Location 3 Figure 3: Space occupied by NMVs from side of carriageway Similar analysis was carried out for the constituents of NMV flow i.e. bicycle and cycle rickshaws. The trend was found to be same at all the locations. In the case of higher side frictions the cycle rickshaws were found moving keeping a buffer at the side of the carriageway. 2.7 MV and NMV Speed Variation The speeds of MVs and NMVs were estimated for the test traps as mentioned before. These, with respect to the total flow on the road segment, are shown in Figure 4. The impact of location and the frictions available at that location was evident from the relative scatter plots of the two categories of the vehicles. Wide differences were observed in the plots between MV and NMV speeds at location 1, which is a two-lane and onedirectional section. The average speed of MVs was found to be around 46 km/h, whereas, that of NMVs was around 16 km/h. A gap of 30 km/h defined the road section category which is NH. The presence of opposing direction flow and the parking on road side at location 2 reduced the gap between the speeds of the two categories. The average speed of MVs was found to be 22 km/h and that of NMVs as 13 km/h, having a difference of only 9 km/h. The impact of much higher frictions at road side at location 3, as already mentioned, reduced the gap to only 5 km/h. The MVs moved at a speed of 13 km/h and NMVs at 8 km/h. The average speeds, their standard deviations and range observed at each of the test location are given in Table 3. 29

36 Effect of Lane Friction on Speed of Non-Motorized Vehicles (a) Location 1 (b) Location 2 (c) Location 3 Figure 4: MV and NMV speed vs total flow Table 3: Speed distribution profile for MVs and NMVs at three locations Data collection location Average speed (km/h) Standard deviation (km/h) Range (km/h) MVs NMVs MVs NMVs MVs NMVs Location Location Location Figure 5 shows the reduction in the speed of MVs and NMVs due to increase in the friction. It was noted that the reduction in MV speeds was following negative exponential. Higher reduction was observed with the presence of opposing traffic and parking of vehicles on road / carriageway side. It was 52 % between location 1 and 2 and 41.5% between location 2 and 3, whereas, in the case of NMVs it was found to be 22.5% and 33% respectively. Figure 5: Reduction in speeds due to presence of roadside frictions 2.8 Speed-Flow Variations The speed-flow data were plotted for the three locations. As mentioned before, the flow was taken in different forms, namely NMV flow, total flow and opposing flow. Total flow considered MV and NMV flow in the direction of movement and opposing traffic flow was considered at location 2 and 3 (both catering to two-directional flow). Figure 6 presents the plot between NMV speed and flows for location 1, 2 and 3 respectively. Behavioural changes were observed when the three plots having similar parameters were compared across the locations. Location 1, being catering to onedirection flow, sufficiently wide and having negligible friction on sides, showed an increase in the NMV speeds with respect to the NMV flow, but a reduction in the NMV speed if total flow was considered. The increase in the side frictions and use of same width by both directional flows, as depicted by location 2, showed an increase in the NMV speeds at lower rate with respect to the NMV flow, as 30

37 NMVs SPEED (km/h) NMVs SPEED (km/h) NMVs SPEED (km/h) NMVs SPEED (km/h) NMVs SPEED (km/h) Effect of Lane Friction on Speed of Non-Motorized Vehicles well as with the total flow. But the presence of opposing flow and an increase in it caused a reduction in the NMV speeds. Another point of observation was the lower dispersion of speed data at location 2 with respect to the location 1. The plot of location 3, which represented the higher level of frictions and space and flow constraints, indicated that even with lower flow values the speeds of NMVs were low, and these reduced with an increase in the NMV, total and opposing flows. Same approach was used to analyze the speeds of bicycles with a change in the flow values (under different forms) and level of frictions imposed by the road side developments. Bicycle flow values were also considered along with the NMV, total and opposing direction flow as per location. These are shown in Figure 7. The speeds were found to be influenced more by road side frictions as compared to the flow constraints or increase in the flow. Bicycle speeds were found to be increasing with bicycle and NMV flow at location 1, but looked somewhat unaffected at location 2 and found decreasing at location 3. The impact of total flow and opposing flow on bicycle speeds was relatively more at location 3 as compared to the other two locations. The variations in cycle rickshaw speeds with flows are shown in Figure TOTLA FLOW (pcu/h) NMVs FLOW (pcu/h) (a) Location NMVs FLOW (pcu/h) TOTAL FLOW (pcu/h) (b) Location CONFLICTING FLOW (pcu/h) 31

38 NMVs SPEED (km/h) NMVs speed (km/h) NMVs SPEED (km/h) Effect of Lane Friction on Speed of Non-Motorized Vehicles NMVs FLOW (pcu/h) TOTAL FLOW (pcu/h) (c) Location CONFLICTING FLOW (pcu/h) Figure 2: Variation in NMV speed with flows (a) Location 1 (b) Location 2 32

39 Effect of Lane Friction on Speed of Non-Motorized Vehicles (c) Location 3 Figure 3: Variation in bicycle speed with flows (a) Location 1 (b) Location 2 33

40 Effect of Lane Friction on Speed of Non-Motorized Vehicles (c) Location 3 Figure 4: Variation in cycle rickshaw speeds with flows Cycle rickshaw speeds were found varying in a range with respect to the flows, the combined trend indicating lesser influence of the locational factors as per the observations made at location 1 (negligible frictions) and location 2 (some frictions). Higher level of frictions and restrictions of space on the carriageway caused a reduction in the speeds of the cycle rickshaws with an increase in the flow values. 2.9 Microscopic Speed Analysis Micro-level analysis of NMV speeds was carried out with respect to the variations in the flow values (in different ranges) and percent share of NMVs in the total flow. These variations are shown in Figure 9 for all the three locations. Under one-directional flow on a two-lane carriageway with negligible side friction (Location 1), the NMV speeds were found increasing with an increase in NMV share in the total flow of upto 1800 pcu/h. Above this value of flow the NMV speeds were found decreasing even with an increase in the percent share of NMVs. On such a road section, if NMV share remains below 30%, then even with an increase in flow the NMV speeds increased. With an increase in side friction and traffic moving in both the directions (Location 2), the NMV speeds were found increasing with an increase in percent share of NMVs for all flow values. An increase in the NMV share above 65% caused reduction in NMV speeds. 34

41 NMVs SPEED (km/h) NMVs SPEED (km/h) NMVs SPEED (km/h) NMVS SPEED (km/h) NMVs SPEED (km/h) NMVs SPEED (km/h) Effect of Lane Friction on Speed of Non-Motorized Vehicles BELOW 1500 pcu/hr ABOVE 1800 pcu/hr pcu/hr TO 1800 pcu/hr PERCENTAGE NMVs SHARE IN TOTAL FLOW BELOW 30% NMVS ABOVE 35% NMVS TOTAL FLOW (pcu/h) 30% NMVS TO 35% NMVS (a) Location 1 below 800 pcu/hr 800 pcu/hr to 1000 pcu/hr above 1000 pcu/hr below 55% NMVs 55% NMVs to 65% NMVs above 65% NMVs PERCENTAGE NMVs SHARE IN TOTAL FLOW TOTAL FLOW (pcu/h) (a) Location BELOW 750 pcu/hr 750 pcu/hr TO 950 pcu/hr ABOVE 951 pcu/hr below 65% NMVs 65% NMVs to 70% NMVs above 70% NMVs PERCENTAGE NMVs SHARE IN TOTAL FLOW TOTAL FLOW (pcu/h) (c) Location 3 Figure 5: Variation in NMV speeds with flow and percent share 35

42 Effect of Lane Friction on Speed of Non-Motorized Vehicles Further increase in the road space and flow constraints (Location 3), in general, showed a reduction in the NMV speeds even with an increase in the NMV shares. 4.0 CONCLUSIONS The study concludes that IRC: , Recommended practice for the design and layout of cycle tracks does not provide any worthwhile and relevant information on the flow characteristic of NMVs. Very little research has been conducted related to the NMV traffic and its flow characteristics in the urban areas. This study tried to provide some input in the area of speed characteristics of NMVs. Some of the findings are as follows: i. High impact of road space and flow constraints was observed on the speeds of MVs and NMVs. This was negative exponential for MVs and almost linear reduction in the case of NMVs. Under the worst conditions of frictions and constraints, the two speeds became quite close to each other. This indicated that under the constraints NMVs are equally efficient as MVs. They keep operating with lower reduction in speeds as compared to MVs. ii. The speed-flow diagrams for the three locations indicated that with the reduction in car and heavy vehicle traffic on a road segment, the NMV speeds picked-up even with increasing total flow. But the increase in the constraints affected them negatively. The presence of opposing flow caused a reduction in NMV speeds irrespective of the level of friction available at a location. The speed-flow relationship for the NMVs cannot be ascertained. This might be due to dispersion of values in close proximity to each other. Looking at this condition an envelope form is suggested to describe the range of values within which the speed of the NMVs can vary for different flow values. iii. The increase in the percent share of NMVs caused an increase in the NMV speeds, on both one-directional and two-directional iv. sections and without or with some friction, for flows upto 1800 pcu/h. Above this flow a reduction was observed in the NMV speeds. Reducing tends were found on sections with high frictions even if the NMV share was quite high in the total traffic. This indicated that space and flow constraints dominate at even low traffic volumes with high share of NMVs. The study has demonstrated that NMV flows are affected more by frictions and constraints rather than traffic volumes. Their share in the traffic volume also impacts the flow characteristics. The study indicated towards provision of dedicated space allocation to NMVs on roads. Under normal traffic flow conditions and negligible side frictions, 2.0 m space was found adequate enough to cater to heavy flows of NMVs. Higher side frictions caused the NMVs to shift towards the centre of the carriageway. Proper enforcement of road side parking outside shoulders and management of shops at the road side might help in restricting them to the marked side strip of 2.0 m from edge. REFERENCES 1. American Association of State Highway and Transportation Officials, Guide for the Development of Bicycle Facilities (1999), American Association of State Highway and Transportation Official, Washington, DC. 2. Duthie, J., Brady, J. F., Mills, A. F., Machemehi, R. B. (2010), Effect of On- Street Bicycle Facility Configuration on Bicyclist and Motorist Behavior, Journal of Transportation Research Record, No. 2190, El-Geneidy, A., Krizek, K. J., Iacono, M. (2013), Predicting Bicycle Travel Speeds along different Facilities using GPS Data: A Proof of Concept Model, Journal of Transportation Research Part D: Transport and Environment, Vol. 16, No. 2,

43 Effect of Lane Friction on Speed of Non-Motorized Vehicles 4. Hongwei,G., Ziyou, G., Xiaomei, Z., Xiaobao, Y., (2011), Traffic Behavior Analysis of Non-motorized Vehicle under Influence of Curb Parking, Journal of Transportation System Engineering and Information Technology, Vol. 11, No. 1, IRC : , Recommended Practice for the Design and Layout of Cycle Tracks, Indian Roads Congress, New Delhi, India. 6. Navin, Francis P.D. (1994), Bicycle Traffic Flow Characteristics: Experimental Results and Comparisons, Journal of ITE. Vol. 64, No. 3, Institute of Transportation Engineers, Washington, DC, Wang, H., Wu, T., (2003), A New United Microcosmic Model of Urban Mixed Traffic Flow, Journal of IEEE,

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45 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 SERVICE QUALITY DETERMINANTS FOR PUBLIC TRANSPORT AND USE INTENTION: A STUDY OF COMMUTERS AND NON- COMMUTERS IN INDIA Dr. Vibhuti Tripathi* and Gunjan Nema** Abstract: Service Quality is one of the important practical themes for transport service providers and regulatory agencies. A better service quality shapes and improves commuters intentions to use public transport services and also encourage the non-commuters to use the services in turn leading to enhanced productivity, profitability and environment protection due to increased usage. Identification of service quality determinants would influence the service improvement initiatives. The paper attempts to address the prime objective of identifying the service quality of determinants for commuters and non-commuters and their influence on use intention. This paper is based on empirical studies conducted in the cities of Delhi, Mumbai, Allahabad and Jabalpur amongst the commuters and non-commuters. It was found that both the groups gave weightage to Availability and Tangibles factors as important service quality determinants though in different order along with Empathy and Responsiveness for Commuters and Safety and Integration for non-commuters. These determinants may be used as the guidelines to improve service quality further by governments or the service providers. Key Words: Urban Public Transport, Service Quality, JnNURM. 1.0 INTRODUCTION Development of transport is considered to be the sine-quanon for the prosperity and smooth functioning of an economy. The modern society in its present form is inconceivable without the development of rapid transportation systems. Rapid growth of population in the cities, the appearance of large manufacturing activities, the fast urbanization of various territories have all contributed to the development of various modes of transport world over (Allen & Thomas, 2000). The economic, social as well as political progress of a country exclusively depends on the progress of transport system, making transport as an essential component of basic infrastructure in modern era. A good urban transport helps to promote urban economy, enables social interactions, increases productivity of resources, provides mobility to people, enables accessibility to opportunities, and sets directions and pattern of growth (Ranganathan N, 1999). According to World Bank Report on, A study on urban transport development, the role of urban transport can be described in a wider context by focusing on issues like inputs for efficient urban development, determinant of the quality of urban life, and as an essential service to the urban poor (The World Bank, 2000). Poor transport systems stifle economic growth and development and the net effect may be a loss of competitiveness in both domestic as well as international markets (Padam, S., 2001). The impacts of a poor urban transport system is manifested in terms of congestion, delays, accidents, high energy consumption, low productivity of resources, high pollution to the environment, inequitable access to services (Ranganathan N,1999) and reduced service quality. The combination of rapid urbanization and motorization has been a key cause of numerous transport problems in developing cities in Asia. It has resulted in a deterioration in accessibility, service levels, safety, comfort, operational efficiency, and the urban environment (A study on urban transport development, The World Bank USA, August 2000). * Assistant Professor, School of Management Studies, Motilal Nehru National Institute of Technology (MNIT), Allahabad, U. P. (India), vibhuti.tripathi@gmail.com ** Research Scholar, School of Management Studies, MNIT, Allahabad, U. P. (India), jan11gunjan@gmail.com

46 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India The usage of public transport in India has been declining over the recent years despite the fact that travel demands have increased significantly. The causes for this decline in India can be attributed to the facts like underdeveloped infrastructure facilities, poor quality of services and socio-cultural stigmas associated with the use of public transport. The Urban Transport System in India lacks the quality and accessibility to match the expectations of passengers and is far behind the international standards. Service delivery of Urban Transport in India is mainly supplyoriented and capital intensive there is a need to develop a market-based and customer-oriented approach to change transport infrastructure services. Satisfaction with services is not rising in line with delivery improvements undertaken through policy initiatives by Government of India. This situation is referred as delivery paradox by Eboli L, Mazzulla G, (2008). According to Low and Gleeson, (2003) customer orientation and localized needs should be the priority while planning public services to be offered. If the service quality is lacking and not well implemented, it will lead to negative perception and dissatisfaction (Karen and Peter, 2007). Research shows that satisfaction with public services has remained static; despite service improvements. Service quality measure thus is a subject of great interest both for planners and transit operators (Eboli L, Mazzulla G, 2008).If the challenges related to service delivery are addressed effectively, it would lead to more efficient use of service resources, increased profitability, improved customer retention, increased customer trust, reduced costs per customer, and reduced turnover. This will also lead to greater passenger satisfaction and less resource wastages on unnecessary improvements and may be a possible answer to the delivery paradox (Blaug et al., 2006). Truly sustainable transportation has not been achieved in any region of the world. It is one of the important practical themes for service providers and regulatory agencies, but it also continues to be a challenging research theme (Tripathi V. et al. 2012). While the service quality determinants have been identified by researchers internationally, a valid model in the Indian context needs to be developed for facilitating the service improvement initiatives. A better service quality will shape and improve commuters intentions to use public transport services and also encourage the noncommuters to use the services. The paper attempts to address the objectives of: 1. Identifying Service Quality Determinants in Commuters and Non-commuters. 2. Exploring the relationship of Service Quality Determinants and Usage Intention. 2.0 LITERATURE REVIEW Service quality as a concept has stimulated an extensive interest and deliberation in varied fields of services due to the complexities posed in measuring and assessing it (Wisniewski, 2001). It can act as a significant differentiator for any service provider (Parasuraman and Zeithaml, 1988). Proponents of this concept initially defined it as the degree to which a customers perception of the service encounter equates or exceeds their expectations for the service (Parasuraman et al., 1985: 18). Alok (2013) defined service quality as the extent to which the service, the service process and the service organization can satisfy the expectations of the user. Service quality is of essence to any industry because it has an effect on customer purchase behaviour and retention (Oh & Mount, 1998). Generally before a customer evaluates service quality, he/she equate the service received with what he/she expected (Voss, Parasuraman& Grewal, 1998). According to Fitzsimmons and Fitzsimmons (1998), there is quality service delivery when perceptions exceed expectations, satisfaction when expectations are met and unacceptable (negative) service quality when expectations are not met. 40

47 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India The provision of good public transport services is critical in alleviating the negative impacts and achieving sustainability s triplebottom-line goals, namely environmental, economic and social goals (Too and Earl, 2009). The measures of public transport service quality have been piecemeal. The important questions relate to establishing a framework for measuring public transport service quality, identifying the top priorities for improving user satisfaction levels with public transport (Too and Earl, 2009), measuring specific requirements of different commuter and non-commuter groups, city specific requirements and measuring specific requirements during different times of the day and locations etc make the entire gamut of service quality delivery in public transportation services a complex issue. This poses a need to identify array of factors or determinants that may influence the service quality of a public transport in India. A number of researchers have provided lists of quality determinants, but the most comprehensive and widely used model of measuring service quality is the SERVQUAL scale proposed by Parasuraman et al. (1985, 1988)based on the disconfirmation of expectations model (Oliver, 1980) is widely used to measure service quality. Parasuraman et al. (1993) hold the view that their SERVQUAL items are the basic skeleton underlying service quality that can be supplemented with context specific items when necessary. A review of literature shows that SERVQUAL (with the 5 RATER dimensions of Reliability, Assurance, Tangibles, Empathy and Responsiveness) has emerged as the predominant model for measuring service quality not only for services in general, but also in the public transport service industry. Allen and DiCesare (1976) considered that quality of service for public transport industry contained two categories: user and non user categories. Under the user category, it consists of speed, reliability, comfort, convenience, safety, special services and innovations. For the non user category, it is composed of system efficiency, pollution and demand. Sillock (1981) conceptualized service quality for pubic-transport industry as the measures of accessibility, reliability, comfort, convenience and safety. According to Middleton (1998a) service quality in public transportation system constitutes of internal and external factors which affect the commuter s perception towards the public transport services. Internal factors such as strategic issues (Lee, Lee, & Lee,2006), top management commitment, service quality standards (Middleton, 1998b), monitoring systems (Deegan, 2002; Gray, 2002; Alexandre & Short, Dec 1995/Jan 1996), customer complaints handling system (Kotler &Kavin, 2008) and external factors such as alternative services (Evans & Shaw, 2001; Michel, 1999), frequency of traveling and timings (Flem&Schiermeyer, 1997; Galetzka, Gelders, Verckens, &Seydel, 2008) convenience and comfort (Regis, 1996), climate, ego, social status, professions(sanchez, 1999) Various dimensions studied by researcher to measure service quality in public transport are summarized in Table 1. Table 1: Different Dimensions Used to Measure Service Quality in Public Transport S.No Author and year Study Dimensions 1 Silcock,1981 Public transport industry Accessibility, reliability, comfort, convenience and safety 2 Hanna and Rail passenger service comfort, timing, cost, location, in transit Drea,1998 quality (Amtrack in US) productivity 3 Drea and Hanna,2000 Rail passenger service quality (Amtrack in US) Non servqual (cost, convenience getting to station, parking availability, comfort, 41

48 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India S.No Author and year Study Dimensions seat comfort, ride, seating area cleanliness, service of on board staff) 4 Tripp and Drea, Cavana RY, Corbett LM and Lo YL,2005(3 column servqual) Rail passenger service quality Rail service quality, (Wellington, New Zealand) Non servqual (announcements, seat comfort, ride, cleanliness of seating area, courtesy of on board staff, rest rooms, café car conditions) Servqual with modification (RATER+ comfort, connection and convenience) 6 LelL,Mac L,2005 Transport service RATER+ loyalty sector(south China) 7 Eboli L, Mazzulla Public transport(italy) Stated preference experiment for G,2008 measuring service quality 8 Too L Earl Public transport(australia) SERVQUAL with the following factors G,2009 -tangibles, responsiveness, reliability and assurance. 9 Prasad MD, Shekhar BR,2010 a 10 Prasad MD, Shekhar BR,2010 b 11 Rita S, Ganesan V, Randheer K, Motawa A, Vijay J, Sezhian M, Muralidharan C, Nambirajan T, Deshmukh SG(2011) Service quality, Indian Railways Service quality, Indian Railways Public transport (India- Chennai) Public transport India(first study in India) Public sector bus transport company, India(SRTU- Tamil Nadu) Servqual with modification(zone of tolerance study) RATER+ comfort RATER+ Service Product, Social Responsibility and service delivery A combination of SERVQUAL and Kano model. 6 factors were Basic services, Appreciative services, Reliability, Assured services, Additional services and Technological advancements. RATER+ culture (excluded Tangibles) customer expectations and company responsibilities Demographic characteristics and public transport specific determinants shape the service quality expectations they also influence the generic dimensions. These factors necessitate to develop type of commuters or city specific measurement scales. Karen and Boo (2007) have suggested that the traditional SERVQUAL dimensions may not be meaningful in all situations and contexts. Svensson (2004) in his study has laid the importance of customizing a particular model to match the study context. Considering the fact a measurement index for service quality of public transport was developed by 42

49 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India the researchers using the basic five SERVQUAL dimensions of Reliability; Assurance; Tangibles; Empathy; Responsiveness. As suggested by Parasuraman et al. (1993). On analyzing the dimensions used by researchers for measuring service quality in public transport in the backdrop of Indian public transport scenario and demography of the country; it is observed that Availability of public transport modes, Integration of multiple modes, Affordability and Safety also play a role in influencing the service quality(tripathi,v. et.al 2012),thus the SERVQUAL was supplemented with 5 more dimensions (Availability, Affordability, Safety, Convenience, Integration) derived through a pilot survey and a structured interview schedule of experts. 3.0 RESEARCH METHODOLOGY The theoretical framework implies testable predictions about the service quality determinants of commuters and non commuters and their influence on use intention. Two Specific predictions related to the relationship are hypothesized as: Table 2: City Characteristics H1: Determinants of Service Quality in Commuters have a direct relationship with use intention. H2: Determinants of Service Quality in Non-Commuters have a direct relationship with use intention. The modified instrument with 10 dimensions was tested for its applicability with the citizens of two Tier 1 cities viz. Mumbai and Delhi and two Tier 2 cities viz. Allahabad and Jabalpur. The sampling was in two stages- firstly purposive sampling method was used to select the cities on the basis of population, implementation of JnNurm, modes of public transport etc. The cities were chosen on the criterion listed in the Table 2. In the second stage the respondents were chosen through convenience sampling method from the public places like markets, bus stops, railway/metro stations etc. A total of 685 responses were collected. TIER I Cities Criteria Mumbai New Delhi Population 19.6 million (Includes Greater 21.7 million (includes Delhi UA, Mumbai UA and Vasai-Virar Faridabad, Gurgaon, Noida,Greater Municipal Corporation)(The Noida and Ghaziabad)(The Economic Economic Times, New Delhi, 20 Oct Times, New Delhi,20 Oct 2011). 2011). JnNURM implementation status implemented implemented Modes of public Suburban trains and buses Buses, metro, ring railway transport available (multimodal transport) (multimodal transport) Current public 45% 43% transport usage Proposed increase to 48% to 45% in usage after JnNURM 43

50 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India TIER II Cities Criteria Allahabad Jabalpur Population 1,117,094(according to provisional 1,054,336(according to provisional census data 2011) census data 2011) JnNURM implementation status implemented implemented Modes of public transport available Buses are the main modes of PT Buses are the main modes of PT Current public 10% 4% transport usage Proposed increase to 12% to 10% in usage after JnNURM A survey was conducted using a structured questionnaire both with the commuting and non-commuting population. Respondents were asked to rate their responses on a five point Likert Scale ranging from Highly Important (1) to Least Important (5). In order to address the research objectives of the paper, the collected data was analyzed with factor analysis and regression analysis using SPSS 16. Collected data was subjected to skewness and kurtosis study to determine normality. It was inferred that the data is normally distributed and thereafter Principal Component Analysis was conducted separately for the data collected for commuters and non-commuters to determine the factors which shape the perceptions of service quality for the two groups separately. The derived factors were considered as independent variables to further test their relationship with dependent variable (Use Intenstions) with the help of regression analysis. Regression helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. 4.0 ANALYSIS AND FINDINGS a. Common Mode of Travel: The respondents were asked to mention common mode through which they commuted within the city. On city wise analysis it was found that despite of an established multi-modal transport system in Delhi the use of public transport as well private vehicles is equally divided and is similar to the non- metro cities of Jabalpur and Allahabad where public transport system is yet to get fully established. (Table 3). Table 3: Most Commonly used Mode of Travel City Public Transport as common mode of travel (%) Mumbai Delhi NCR Allahabad Jabalpur Total Own Vehicle as common mode of travel (%) b. Distance travelled per trip: On asking the respondents to mention the distance they travelled per trip it was found that maximum percentage of (22.9%) travelled more than between 5-10 kms. per trip. It is also evident from Table 3 that trip distance of the respondents was more in metro cities in comparison to non-metro cities of Jabalpur and Allahabad. 44

51 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India Table 4: City wise kilometers travelled per trip City Less than 5 km (%) 5-10 km (%) km (%) km (%) km (%) More than 25 km (%) Mumbai Delhi NCR Allahabad Jabalpur Total c. Determinants of Service Quality in Commuters: Out of the total sample, the data of 388 respondents was further treated with Principle Component Analysis. Bartlett s test of sphericity and Kaiser- Meyer Olkin (KMO) measures of sampling adequacy were used to examine the appropriateness of Factor Analysis. The KMO statistic (0.942) is large and significant (>.05), considering the value Factor Analysis is considered as an appropriate technique for further analysis of data. KMO value closer to zero indicates that a diffusion exists in the pattern of correlations and suggests that factor analysis will not be appropriate for the sample (Field, 2009).As recommended by Kaiser, 0.5 is the lowest threshold for proceeding further. Values between.5 and.7 as mediocre, upto.8 as good, upto.9 as great and above.9 as excellent (Field, 2009). A significant Bartlett s test (p<0.001) indicates that correlations between items are sufficiently large to proceed with the PCA. KMO value is.942 and is excellent (Field, 2009). Nine factors were extracted using the scree plot criteria which explained % variance in the data. According to Howitt D, et.al it is more helpful to use the screen test in order to estimate the number of factors. Cronbach Alpha values were in the range of.877 and.736 indicating high internal consistency reliability. Table 5: KMO and Bartletts Test (Commuters) Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test Approx. of Sphericity Square Chi Df Sig..000 Factor loadings in the range of.30 to.40 are considered to meet the minimal level for interpretation of structure,.50 or greater are considered practically significant. Loadings above.70 are indicative of a well defined structure (Hair et al, 2009).Individual items were checked for factor loadings and the items with loadings below.50 were dropped from further analysis. Table 5 shows the factor structure of commuters and the nomenclature given to each of the extracted factor. Table 6: Factor structure (Commuters) S.no Factor Items Factor Loadings Cronbach alpha 1 Empathy Employees are neat and well dressed Driver, conductor and other employees are.632 courteous and helpful. Employees are prompt in responding to

52 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India S.no Factor Items Factor Loadings Cronbach alpha commuters. The staff has enough knowledge and skill.581 to handle customer queries. 2 Responsiveness There is provision to register complaints Any complaint lodged is properly.764 addressed. Ample staff is available to handle requests.755 and complaints. First aid facilities are adequately available..734 The frequency of breakdown is low and.518 there is enough back up service. Special services are planned according to.531 specific needs of various commuter groups (old, disabled, women and children). 3 Convenience Refreshment shops are in adequate numbers. Cleanliness is maintained at the.567 stops/stations. Proper lighting is maintained at the.646 stop/station. There are sufficient number of ticket.583 windows. Modernised ticket dispersal mechanism is.674 available. It is easy to use the ticket dispersal.570 mechanism. Proper ticket is issued in time Availability In case of changing routes there are enough connecting public transport options available. The various public transport modes.571 available in the city are well integrated. In case I have to switch mode, I don t have.553 to wait for long. Government public transport is available.737 in all parts of the city. Government public transport running on.746 different routes is sufficient. Government public transport is available.695 at all times of the day. 5 Tangibles Parking facility near the boarding point is adequately available. In case I travel with my private vehicle to the boarding point, I can easily park it

53 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India S.no Factor Items Factor Loadings Cronbach alpha nearby. The government public transport vehicles.513 are spacious. The public transport vehicles are well.549 maintained. There is enough provision to carry.537 luggage. Adequate seats are available to commuters.506 waiting at the boarding point of government public transport. 6 Affordability Tickets are affordable Daily/monthly travel passes are available..708 The passes are affordable Assurance Time schedules and fare charts are displayed at adequate places. Time schedules and fare charts are simple.676 to understand. Adequate number of announcements are.735 made. Any change in the time table is properly.653 communicated. 8 Safety I feel safe while using the public transport facilities. I feel safe while using the public transport.751 facilities at night also. Night services are reliable..682 In order to test the following hypothesis: H 1 : Determinants of Service Quality in Commuters have a direct relationship with use intention. Mode l R R Squar e Adjusted R Square Table 7: Model Summary Std. Error of the Estimate R Square Change A stepwise multiple regression was performed. The most important predictors were entered stepwise and the other predictors being non-significant are removed from the analysis. Change Statistics F Change df 1 df2 Sig. F Change a b c Durbin- Watson d a. Predictors: (Constant), F1 b. Predictors: (Constant), F1, F5 47

54 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India c. Predictors: (Constant), F1, F5, F6 d. Predictors: (Constant), F1, F5, 6, F4 e. Dependent Variable: DV Model Table 8: Coefficients Unstandardized Coefficients Step 1 (Constant) Standardized Coefficients B Std. Error Beta Empathy Step 2 (Constant) Empathy Tangibles Step 3 (Constant) Empathy Tangibles Responsiveness Step 4 (Constant) Empathy Tangibles Responsiveness Availability R 2 =.206, for Step 1, Δ R 2 =.046 for Step 2, Δ R 2 =.037 for Step 3, Δ R 2 =.021 for Step 4 The standardized beta coefficients show the relative impact on the dependent variable of a change in 1 standard deviation in either variable (Hair et al, 2007). The beta values indicate the individual contribution of each predictor to the model. The standardized coefficient beta for model 4 for f1 is.167, for f5 is.230, for f6 is.166 and for f4 is.195.the corresponding t values for all these predictors are significant at (p<.05) as per the recommendations of (Field, 2009).All predictors are hence significant predictors of the model. The following hypothesized relationships were assessed using Stepwise Multiple Regression: 48

55 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India Hypothesis Predictor/Independent Direction of Dependent Regression analysis variable relationship variable for hypothesis H 1a Empathy Use Intention Supported H 1b Responsiveness Use Intention Supported H 1c Convenience Use Intention Not Supported H 1d Availability Use Intention Supported H 1e Tangibles Use Intention Supported H 1f Affordability Use Intention Not Supported H 1g Assurance Use Intention Not Supported H 1h Safety Use Intention Not Supported The following regression model is obtained for commuters- d. Determinants of Service Quality in Non- Commuters: Out of thetotal sample, the data of 279 respondents was further treated with Principle Component Analysis. Bartlett s test of sphericity and Kaiser- Meyer Olkin (KMO) measures of sampling adequacy were used to examine the appropriateness of factor analysis. The KMO statistic (0.901) is large and significant (>.05), a significant Bartlett s test (p<0.001) indicates that correlations between items are sufficiently large to proceed with the PCA. 9 factors were extracted using the scree plot criteria which explained % variance in the data. (Howitt D, Cramer D, 2011) say that it is more helpful to use the scree test in order to estimate the number of factors. Cronbach Alpha values were in the range of.695 and.866 indicating high internal consistency reliability. Table 9: KMO and Bartletts Test (Non- Commuters) Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi- Square Df Sig..000 Table 9 shows the factor structure of Commuters and the nomenclature given to each of the extracted factor. 49

56 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India S.no Factor Items 1 Tangibles Table 10: Factor structure (Non-Commuters) Factor loadings The government public transport vehicles are spacious The public transport vehicles are well maintained.539 Seats in the government public transport vehicles are comfortable..561 There is enough provision to carry luggage..659 The government public transport vehicles are punctual..661 Cleanliness is maintained at the stops/stations..654 Proper lighting is maintained at the stop/station..704 In case I travel with my private vehicle to the boarding point, I can easily park it.706 nearby. Parking facility near the boarding point is adequately available..711 The boarding points are well maintained..614 There are sufficient numbers of ticket windows..555 Cronbach alpha 2 Integration 3 Availability In case of changing routes there are enough connecting public transport options available. The various public transport modes available in the city are well integrated. In case I have to switch mode, I don t have to wait for long. The government public transport stops for sufficient time for boarding and unboarding. It is easy to board and unboard the bus/train. Government public transport is available in all parts of the city. Government public transport running on different routes is sufficient. Government public transport is available at all times of the day

57 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India 4 Empathy Employees are neat and well dressed Driver, conductor and other employees are courteous and helpful Responsiveness There is provision to register complaints Ample staff is available to handle requests and complaints. First aid facilities are adequately available. The frequency of breakdown is low and there is enough back up service. Special services are planned according to specific needs of various commuter groups (old, disabled, women and children) Affordability Tickets are affordable Daily/monthly travel passes are available..684 The passes are affordable Assurance Time schedules and fare charts are displayed at adequate places. Time schedules and fare charts are simple to understand. Adequate number of announcements are made. Any change in the time table is properly communicated Safety I feel safe while using the public transport facilities I feel safe while using the public transport facilities at night also..718 Night services are reliable..559 In order to test the hypothesis H 2 : Determinants of Service Quality in Non-Commuters have a direct relationship with use intention. A stepwise multiple regression was performed to ascertain the important determinants of public transport service quality among non-commuters. The following relationship was thus revealed. 51

58 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India Table 11: Model Summary e Model R R 2 Adjusted Std. Error Change Statistics Durbin R 2 of the Estimate R 2 Change F Change df1 df2 Sig. F Change - Watson a b c d a. Predictors: (Constant), f7 b. Predictors: (Constant), f7, f2 c. Predictors: (Constant), f7, f2, f8 d. Predictors: (Constant), f7, f2, f8, f3 e. Dependent Variable: dv Table 12: Coefficients a Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta 1 (Constant) Safety (Constant) Safety Assurance (Constant) Safety Assurance Tangibles (Constant) Safety Assurance Tangibles Integration The following hypothesized relationships were assessed using Stepwise Multiple Regression Hypothesis Predictor / Direction of Dependent Regression analysis Independent variable relationship variable for hypothesis H 2a Tangibles Use Intention Supported H 2b Integration Use Intention Supported H 2c Empathy Use Intention Not Supported H 2d Responsiveness Use Intention Supported H 2e Affordability Use Intention Not Supported H 2f Assurance Use Intention Not Supported H 2g Safety Use Intention Supported The following regression model was obtained for non-commuters- 52

59 Service Quality Determinants for Public Transport and Use Intention: A Study of Commuters and Non-Commuters in India The predictors in the model explain 39.8% variance in the dependent variable iebehavioral intentions. 5.0 CONCLUSION The study reveals that barring Mumbai which has a well established and integrated public transport in all other cities of Delhi, Allahabad and Jabalpur respondents used public transport and private vehicles equally. It can be attributed to the fact that the accessibility of Public Transport in the city and sub-urban areas is limited to certain parts only also that the public transport system is yet to get further developed by improvements in infrastructure and networks in comparison to Mumbai, where the share of respondents using public transport is 83.9%. The study indicates that the commuters assign more weight to Empathy (Employee Related Aspects etc.) followed by Tangible (Condition of the Vehicles, Boarding Points etc.), Responsiveness (Complaints Handling, Special Services etc.) and Availability (Sufficient routes, Available all times etc etc.) factors. Whereas other factors of Convenience, Assurance, Affordability and Safety are not as important service quality determinants. Noncommuters pay maximum importance to Integration (Different Routes, Connecting to different modes etc.) while the next important was Availability (Sufficient routes, Available all times etc) followed by Tangibles (Condition of the Vehicles, Boarding Points etc.) and Safety factors (Safety while using public transport, safety during Night use etc.). Both the groups of commuters and noncommuters indicate a separate set of service quality determinants; they give importance to Availability and Tangibles though in a different order. Empathy and Responsiveness are other two important determinants of service quality for commuters. Safety and Integration are the other important service quality determinants for Non-commuters. It can be concluded that public transport service providers need to understand and provide reliable services to the commuters consistently. A better service quality will shape and improve passenger intentions to use public transport services and attract a large number of people to minimize the use of privately owned transport. Identification of service quality determinants influences the service improvement initiatives. When effective and efficient systems are put in place gradually to monitor the service quality determinants the desired goal of providing quality of service can be achieved which will also address issues of urban pollution and traffic congestion in most of the cities. These determinants may be used as the guidelines to improve service quality further by governments or the service providers. REFERENCES 1. Alexandre, A., & Short, J. (Dec 1995/Jan 1996). Can pricing change urban travel? Organization for Economic Cooperation and Development. The OECD Observer, 197, Allen, W.G., &Dicesare, F. (1976). Transit Service evaluation: preliminary 53

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63 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 GENERIC FRAMEWORK FOR ESTIMATING CARBON FOOTPRINT OF COMMUTING WITH PUBLIC TRANSPORT MODES Kirti Bhandari*, Mukti Advani**, Purnima Parida*** Abstract: Changes in climate caused by changes in anthropogenic (i.e. man-made ) greenhouse gas (GHG) emissions have become a major public policy issue in countries all over the world. With an estimated 28.4% of these emissions attributed to the transportation sector, attention is being focused on strategies aimed at reducing transportation GHG emissions. Quantifying the change in GHG emissions due to such strategies is one of the most challenging aspects of integrating GHG emissions and climate change into transportation planning and policy analysis. This research aims to develop a method for estimating the carbon footprint of commuting and apply this method to the public transport systems existing in Delhi. A complete study on trip profile of the transit commuters of available modes will be used to estimate the carbon foot prints for different modecombinational trips (trip profile including access, egress and main line haul mode). This methodology consists of estimating the number of trips by each mode followed by estimating the direct CO2 emissions. Carbon footprints provide insights into the potential impact of different policies. Questions such as where to apply certain policies (both in terms of mode and geographic area) to gain the largest reductions can be answered using such footprints. Keywords: Framework, access, egress, trips, emission factors, CO2 emissions 1.0 BACKGROUND Transport sector contributes around 14% towards the global emissions of greenhouse gases [World Bank, 2011]. Carbon dioxide represents the largest proportion of basket of greenhouse gas emissions. During, the past three decades, carbon dioxide emissions from transport has increased faster than those from all other sectors and are projected to increase more rapidly in coming years, if no intervention is done.. The road transport alone emits around 16% of the global CO 2 emissions [IEA, 2007]. From 1990 to 2004, carbon dioxide emissions from the world s transport emissions have increased by 36.5%. As one of the most rapidly growing countries and the fifth largest CO 2 emitter in the world, India is experiencing a rapid growth in its economy as well motorized mobility [OICA]. Passenger mobility in Delhi is poised to increase at the rate of 8.7% reaching 534 billion pkm by 2020 [Bhandari, K. and Y. Hayashi (2011)]. Buses form the backbone of public transport, but remain very unreliable, overcrowded and inefficient; this has resulted in increased usage of personalized modes of transport and its environmental consequences. In this light the Government of India has announced a national urban transport policy in April 2006 as an integral part of the Jawaharlal Nehru National Urban Renewal Mission (JNNURM). The draft National Urban Transport Policy (NUTP) aims at curtailing the use of private vehicles and give impetus to public transport and non-motorized vehicles. The policy envisages encouraging 4 million plus cities to plan for a mass transit system adopting a technology that best suits the city requirement. The options for this include buses on dedicated corridors, elevated sky bus and monorail systems, electric trolley buses and metro systems. 2.0 INTRODUCTION Worldwide, energy use is increasing faster in the transport sector than in any other sector, and fastest of all in developing countries. From * Principal Scientist, Environmental Sciences Division, CSIR-Central Road Research Institute, New Delhi ; kirti.bhandari7@gmail.com ** Scientist, Transportation Planning Division, CSIR-Central Road Research Institute, New Delhi ; mukti7@gmail.com *** Head, Transportation Planning Division, CSIR-Central Road Research Institute, New Delhi ;punam31@gmail.com

64 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes 1980 to 1997, transportation energy use and associated GHG emissions increased over 5 percent per year in Asia compared to one percent growth in greenhouse gases from all sectors worldwide. Transport situation in most Indian metropolitan cities is rapidly deteriorating because of the increasing travel demand and inadequate public transportation system and national capital Delhi is no exception to it. A large proportion of Delhi s population commutes daily for different purposes like work, education, recreation etc. The mode of transport chosen for commuting by each individual depends on his socio-economic characteristics and the availability of the modes. Through this study, an attempt has been made to identify various factors that affect an individual s mode choice and then estimate the carbon footprint due to the choices made under different scenarios with main focus on the public transport modes that are made available to each individual in Delhi. 3.0 OBJECTIVES The aim of the study is to assess urban mass transport systems in relation to travel mode choice for commuting trips. Using carbon footprint concept to evaluate sustainability, it is possible to represent and communicate effectively the issues of environmental impact and sustainability. The main objective of the study was to focus on estimating the carbon footprint due to commuting focusing on the public transport modes. The study also estimated the environmental impact of different travel options available for commuters. More importantly, the study evaluated the impact of Zero carbon modes such as walking, cycling and rickshaws on carbon footprint of commuting. 4.0 RESEARCH FRAMEWORK The framework, as shown in Figure 1, is designed to be carried out in three phases, starting from the pre-analysis phase which includes a general description of the city transport system, data collection through passenger interview survey followed by compilation and preliminary analysis. Technical part deals with the mode choice modelling, that acts as a major tool to estimate the probable division of mode choice between the two alternatives of public transit. The mode choice models include socio-economic variables to account for differences in individual preferences and level-of-service variables to measure the relative distributes of public transit. Analysis part deals with the estimation of carbon footprint of commuting during each trip in the study area, i.e. Delhi using the data collected from the passenger interview survey and forming different scenarios for the comparative analysis of the carbon footprint estimated. 5.0 METHODOLOGY 5.1 Study area In Delhi there has been a major improvement in transport infrastructure in recent years in terms of construction of flyovers, road widening, new linkages and operation of metro rail along major travel corridors. The unprecedented increase in population, number of vehicles and trips has put a tremendous pressure on demand of road infrastructure, but due to resource crunch the supply has not been able to match the demand. This has forced the existing network system function beyond its capacity and has manifested itself in the form of serious traffic problems like congestion, delays, safety, excessive fuel wastage and environmental pollution. 58

65 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes 5.2 Data collection Passenger interview survey was conducted at 77 metro stations alongwith adjacent bus stops for a comparison between the two public transport modes. The survey areas were selected with an objective to collect samples from different socio-economic background and the areas which are spatially distributed along the yellow and blue metro line in Delhi as indicated in figure 2. The yellow and the blue lines were chosen for the survey as they represent the North-South and East-West corridor of the city. The targeted individuals for the information collection were workers and students aged above 15. The survey questionnaire was divided into 4 sections. Figure 1: Conceptual Framework These were the socio-economic parameters, trip characteristics, current travel choice and their willingness to change to the other mode. 5.3 Modes and Technology Mix Figure 3 shows the details of the technology type of various modes in Delhi, which have also been adopted in this study. The public modes comprise of Bus and MRTS which run on CNG and Electricity respectively whereas personal transport modes comprise of two-wheeler, motorcycles and cars. The electricity generation mix in India is as follows: 70 percent coal, 15 percent hydroelectricity, 10 percent natural gas, and 5 percent others (mostly petroleum & biomass). 59

66 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes Figure 2: Survey Area for Data Collection Figure 3: Modes and Technology Types Mix used in Study As per the choice set design in the specified by the respondent for his/her trip and some from the attached table. To estimate the values of cost of each OD pair for car, auto rickshaw and two-wheelers, the data of fuel efficiency, given in Table 1 and distance traveled is used. The average operating speeds for metro is known from the literature along with bus, car, auto and walking. The speed of cycling is thrice that of walking and is used for determination of speed of cycle and cycle rickshaw, whereas, the speed of two wheeler is taken as average of car and metro. Information regarding the fare structure of public bus and MRTS system is given in table 2. The distance between origin and destination zones is estimated using Google Maps. Similarly, the distances between the given OD pairs on the bus network and the metro are also estimated. Using the information on the distance and the speed of particular mode (Table 3), the values for travel time is estimated. Finally, the values of travel time and travel cost for car, rickshaw, auto rickshaw, two-wheeler, bus and metro are used along with other specified variables, to estimate the utility of each mode. questionnaire, some variable values were Vehicle type Table 1: Mode characteristics in Delhi Occup ancy Fuel Efficiency (Km/lit) Vehicle utilization( Km/year) Car ,500 Two ,000 wheeler Three ,000 wheeler Bus ,000 Source: Bose and Srinivasachary (1997) 60

67 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes Table 2: Fare structures of the public bus service and MRTS Bus MRTS Up to 4 Rs 5 Minimum Rs 8 Km 4-10 Km Rs 10 Maximum Rs Km Rs onwards 15 Table 3: Journey Speed for vehicles Vehicle Speed(Km/hr) Metro 33* Two wheelers and Motorcycles 32 Car 30** Three wheelers 25 Bus 21*** Cycle 10.5 Cycle rickshaw 7 Walk 3.5 Source: *Gwilliam, K. (2002), **CDP (2006), ***CRRI(2003) 6.0 DATA ANALYSIS Socio-economic character and the mobility of the people are directly related to each other. The more mobile a person is, the wider the circle of socio-economic interaction that would be available to them. In turn, both mobility and socio-economic status influence the type, frequency and intensity of their participation in activities. Individual income is the indicator of socio-economic status. Private vehicle ownership and public transport accessibility are the indicators of travel behaviour. 6.1 Socio-economic and household characteristics Socio-economic and household characteristics such as gender, age, occupation, household size, household income, vehicle ownerships, play an important role in the travel characteristics. Gender and age distribution of sample are shown in Table 4. Out of the total responses, majority of the respondents are working. Majority of workers are males (80%) and accordingly have higher percentage in target group. As per age, most of the individuals belong to the category of and years of age. Overall, most of the households belong to the middle income group having monthly income in the range of Rs. 10,000 to 20,000 per month. The average household size comes around 4 members per household. 6.2 Vehicle Ownership Table 5 shows the vehicle ownership for sample households. As vehicle ownership increases, the chances of using public transport (bus and metro) also decreases. In most of the cases the vehicle owned by a household is available to head (male) of the household while remaining members rely on the public transport. Household vehicle availability for different public transport users is given in Table 6. Table 4: Socio-economic Characteristics of Sample Population Item Absolute Values (N) Relative Values (%) Socio-economic characteristics No. of individual 4,771 observations Sex Male 3, % Female % Age , % , % % Table 5: Household vehicle ownership Vehicle Count % Car % Motor scooter % Bicycle % Others % No vehicle % 61

68 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes Table 6: Household Vehicle Availability Public Transport users Household Vehicle Availability BUS Total Absolute Values (N) Relative Values (%) % METRO Total Absolute Values (N) Relative Values (%) CARBON FOOTPRINT Public transport and non-motorised modes play a significant role in providing sustainable transport. There is a lack of comprehensive detailed study which focuses on the carbon footprint of different modes of travel. Earlier work by Bhandari et. al (2010) focuses on the environmental implications of passenger mobility in Delhi with focus on the ecological footprint of commuting. This study focuses on estimation of carbon footprint based on complete trip profile from origin to destination including the access and egress trips and carbon footprint of different modes of travel. Figure 4 shows the methodology adopted for estimating the carbon footprint of travel. The entire trip from origin to destination is considered and the mode used in each segment is considered. The energy and CO2 emissions from each segment are thus estimated to derive the final emissions from the trips. Table 7 shows the average emission factors for Indian vehicles developed by the Automotive Research Association of India (ARAI) in Table 8 gives the average Figure 4: Methodology for calculating Carbon Footprint emission factors of CO2 in tonnes/tj for each type of fuel. Using the fuel economy for each type vehicle the emission factor for CO2 is derived which is vehicle and fuel specific. Table 7: Mode Specific emission factors for Indian vehicles S.No Mode Type CO 2 (gms/km) 1. Moped 2 Stroke Stroke Two Wheeler 2 Stroke Stroke Motor Cycle 4 Stroke Car Petrol

69 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes S.No Mode Type CO 2 (gms/km) Diesel CNG Three Wheeler CNG Bus CNG Source: ARAI draft report on factor development for Indian vehicles (2007) Table 8: Emission factor for each pollutant in Ton/TJ S. CO 2 Emission Occupancy Mode Fuel No. (ton/tj)* factor (g/km) ** CO 2 (g/pkm) 1 Car Gasoline Diesel CNG Two wheeler Gasoline Three Wheeler CNG Bus CNG Metro Electricity *** 1.27 Source: *Journal of Urban Planning and Development, Vol 136, No.1, March 1, 2010, pp 89 (2010) **source of occupancy figures: Bose and Srinivaschary (1997) *** 7.1 CO2 reduction for bus and metro trips The pie chart (fig 5) clearly shows that, out of total data available for all segments- 58% of trips used metro as main haul mode whereas 42% data were main trip by bus. CO2 emission was then calculated in gram per passenger for access, egress and main trip, by multiplying the distance, by respective emission factor (given in table 8) on the basis of mode used. To do this two scenarios were considered (for access & egress trips) for both bus and metro trips. In scenario 1 CO2 emission for both access and egress trips was estimated for the modes being presently used by both the metro and bus users. In scenario 2 all the motorised access and egress trips whose distance is <=2 km are converted into NMT. The difference in CO2 of scenario 1 and scenario 2 for both access and egress shows the amount of CO2 emission that can be reduced or saved if we shift to non motorised trips for distance <=2 km (Table 9). The reduction in CO2 for access and egress trips for metro is higher, clearly indicating that the number of motorised trips/modes being used to access the metro and to finally reach the desired destination is higher as compared to the bus as the main haul trip. This indicates first and the last mile connectivity for public transport trips, which are heavily dependent on the motorised modes in case of metro. Therefore, by providing better NMT facilities and infrastructure for NMT modes (walk, cycle, cycle rickshaw) around metro stations, carbon footprint of commuting can be reduced considerably. 63

70 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes have made their access and egress trips by NMT modes whereas 87% of bus trips have made their access and egress trips by NMT modes. This clearly shows that a larger number of access and egress trips are made by motorised modes in case of metro. Figure 5: Metro and bus users in the sample Table 9: CO2 reduction for access and egress trips CO 2 reduction Trips A+E (g/passengerkm) Bus Metro Segment Segment Segment Segment Segment Total for trips 8.0 ANALYSIS OF TRIPS WITH THREE SEGMENTS The data is segregated in terms of the number of trip segments. The total number of trips finally considered is Table 10 shows the number of trips of each segment, with the minimum being 3 segments and the maximum being 7 segment trips. The three segment trips were then categorized into 4 categories depending on the mode for access and egress trips. These categories are NMT- Metro, NMT-Bus, MT-Metro and MT-Bus. The number of trips for each of these categories along with their frequencies is given below in the table 11. Further, table 12 shows the percentage of trips where access and egress trips with motorised and non-motorised modes. Table shows that 75% of metro trips Table 10: Number of trips for each segment No of Finally Data segments in considered available a trip Total Table 11: Categories for three segment trips based on access and egress modes Category Number of trips Frequency NMT-Metro NMT-Bus MT-Metro MT-Bus Total metro Total bus Table 12: Mode for Access Egress trips Total no of trips AE trips with NMT modes AE trips with MT modes Case-1: As the trip length increases, proportion of AE distance with respect to total distance decreases. However mostly this remains low for NMT bus trips compared to the NMT Metro trips. This gap is negligible for trips above 10 km. This is shown in figures 6(A) and 6(B). 64

71 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes Figure 6(A): Share of Access and egress trips distance in total distance by NMT modes Case-2: The share of AE time with respect to total trip time is shown in figures 7(A) and 7(B). As the trip length increases, proportion of AE time with respect to total time decreases. However this always remains low Figure 6(B): Share of Access and egress trips distance in total distance by MT modes for NMT bus trips compared to the NMT Metro trips. The gap between NMT Metro and NMT bus trips remains nearly the constant. This indicates that it is easy to get a bus using NMT modes as feeder than to get a metro. Figure 7(A): Share of Access and egress trips time in total time by NMT modes Case-3: There is no clear relationship observed for the cost factor as shown in figures 8(A) and 8(B). This includes the cost Figure 7(B): Share of Access and egress trips time in total time by MT modes. attached with the A&E trips which have been made by NMT modes. This highlights the need of further work. This study would be further extended by separate analysis for each type of NMT modes used for making these A&E trips. Figure 8(A): Share of Access and egress trips time in total cost by NMT modes. Figure 8(B): Share of Access and egress trips distance in total cost by MT modes 65

72 Generic Framework for Estimating Carbon Footprint of Commuting with Public Transport Modes 9.0 CONCLUSIONS The transport sector has a potential impact on the GHG emissions and ultimately results in Climate Change. This sector contributes about 14% to the global GHG emissions. Thus, curbing these emissions has become an area of concern for transport planners, engineers and environmentalists. Only the aggressive strategies can slow down the pace of increase in GHG emissions. This study focused on two different aspects of commuting, one the mode choice for each individual from a given set of options and the other carbon footprint accrued due to their commuting choices. The study results showed that 28% of the commuters were willing to shift to metro and the carbon emissions occurring because of bus users was way higher than those of metro users. Thus, the shift of commuters from the carbon intensive mode i.e. bus to metro would further help in reducing the impact of commuting in terms of carbon emissions. But reducing the CO 2 emissions by upgrading commuting to greener transportation modes will require an array of coordinated, progressive transportation policies, supplemented by public outreach campaigns like, educating people on carbon impacts of commuting by personalized or road based public transport modes as well as benefits of using the Zero- polluting commuting options. The trips performed for work and education purpose are consistent, predictable, performed alone and generally within manageable distances, therefore, there is a scope to alter commuting trip by way of switching to a different mode as compared to other less predictable trips. Moreover, the repetitive nature of trips implies that huge benefits in terms of carbon footprints can be accrued from seemingly small changes. REFERENCES 1. Automotive Research Association of India (ARAI) (2007). Emission factor development for Indian vehicles. 2. Bhandari, K., Shukla, A., Gangopadhyay, S., Hayashi, Y.(2010). Environmental implications of passenger mobility in Delhi: Energy consumption, CO 2 emissions and ecological footprint of commuting, 3 rd international conference of Transport Science and Technology Congress, Apr 4-7, New Delhi. 3. Bhandari, K. And Hayashi, Y. (2011). Mass Rapid Transit and its Impact Assessment: Case of Delhi, Economy, Equity and Environment, LAP LAMBERT Academic Publishing. 4. Bose, R. K. and Srinivasachary, V.(1997) Policies to reduce energy use and environmental emissions in transport sector A case of Delhi city, Energy Policy, Vol. 25(14-15), pp Central Road Research Institute (CRRI) (2003), Urban Road Traffic and Air pollution. 6. Gwilliam, K. (2002). Cities on the move:urban transport strategy review, World Bank, Washington D.C. 7. Han, J, Bhandari, K. And Yoshitsugu Hayashi. (2010) Assessment of Policies towards an environmentally friendly urban transport system: Case study of Delhi, India, Journal of Urban Planning and Development, Vol 136, No.1, March 1, pp IEA (2007). CO 2 emissions from fuel combustion, : 2007 edition, IEA Paris 9. RNAL/TOPICS/EXTTRANSPORT/0,,cont entmdk: ~menupk:337124~page PK:148956~piPK:216618~theSitePK: ,00.html last accessed April 17, last accessed on August 28,

73 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 MODELING MODE CHOICE BEHAVIOUR AND ESTIMATING VALUE OF TRAVEL TIME OF COMMUTERS IN DELHI Minal* and Ch.Ravi Sekhar** Abstract: Dealing with the present bottlenecks as well as creating long lasting and sustainable transport systems has been the greatest challenge of urban transport planning. Calibrating the present need and forecasting the future demand is the underlying agenda of travel demand forecasting. Mode choice forms an integral part of this process as it gives a complete insight to the mode choice preferences of the commuters validating the introduction of new transport systems to existing ones. This study aims at modelling the mode choice of commuters in Delhi. For this discrete Multinomial logit has been considered and mode choice analysis has been carried out. An extensive household survey has been carried out and disaggregate data was collected in various localities of Delhi. Thirteen explanatory variables were considered which includes household information, personal information and trip information for modeling mode choice behavior. Value of travel time has been quantified separately for motorized and non motorized mode of commuters. The value of in-vehicle travel time estimated for motorized vehicle is found to be 95 Indian Rupees ( ) per hour. The value of total travel time estimated for non-motorized vehicle is found to be 451 per hour. Keywords: Mode Choice, Delhi, household survey, multimodal logit, value of travel time 1.0 INTRODUCTION Transportation community is bound to face challenges that are both dynamic in nature and futuristic in its application perspective. Amongst the various confluences in Transportation system, congestion is by far the most common and difficult factor to overcome. Congestion drastically affects the level of service of the transport system leading to consequences like delay, accidents which lead to huge economic loss every year. To alleviate the situation studying the travel behaviour and choice of commuter is useful. The ultimate interest lies in being able to predict the decision making behavior of the commuters while taking under consideration the attributes of different modes like cost, safety, convenience and travel time. Mode Choice problem has been approached by transportation planners in many different ways. In a broad way all these approaches can be classified into two categories namely discrete choice models and non-discrete choice models. Discrete choice models primarily include Multinomial Probit model, Multinomial Logit model (MNL) and Nested Logit (NL) model. Non-discrete choice models include regression approach, cross classification tables and diversion curves. The objective behind mode choice model is to effectively manage the demand and be able to provide for these demands by making changes in the existing system. In Delhi major modes of transport are private cars, two wheelers, bus, metro and auto rickshaw (three wheeler) and bicycle. Delhi with a population of 16.8 millions (Census, 2011) is under constant need of expansion of existing transport facilities. Attracting the users of private modes to mass transport modes like bus and metro seems to be a solution but is not very feasible given the comfort factor of mass transport facilities.the objective of the present study was to develop a mode choice model for commuter of Delhi by considering most widely used Multinomial Logit (MNL) model. Data collection of the disaggregate data was done through an extensive household survey in Delhi to account different strata of population with * M.Tech Student, Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Road Research Institute New Delhi , minal.crri@gmail.com ** Senior Scientist, Transportation Planning Division, CSIR-Central Road Research Institute, New Delhi 10025, chalumuri.ravisekhar@gmail.com

74 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi different socioeconomic backdrops, age and gender. The relative influence of various parameters associated with different modes also been estimated and it s interpretation has been elaborated. This will help to policy makers or transportation engineers/planners for further improvements and amendments of the transit facilities in Delhi. The value of travel time has also been estimated in this study to quantify the economic measure of the commuters travel time. 2.0 LITERATURE REVIEW Mode choice model initially proposed by Adam (1959) to investigate factors influencing mass transit and automobile travel in urban areas. Mode choice models are two folds aggregate and disaggregate models. Aggregate models are based on zonal or inter zonal information and disaggregate models are based on household or individual data also referred to as micro data. Disaggregate models associated advantages over aggregate models, has led to the widespread use of disaggregate discrete choice methods in travel demand modeling, destination choice (Koppleman and Bhat,2006), route choice (Gliebe et. al, 1998), air travel choices (Proussaloglou et. al,1999) activity analysis (Wen et. al, 1999) and auto ownership, brand and model choice (Bhat et. al, 1997). Bhat (1995) developed a heteroscedastic extreme value model of intercity mode choice in which estimation of the ridership share on a proposed new intercity travel service was done. Identification of the modes from which existing intercity travellers will be diverted to the new or upgraded service was performed. Five different models in the study were used there were multinomial logit model, three possible nested logit models, and the heteroscedastic extreme value model. The resulting heteroscedastic extreme value model has a number of advantages over other commonly used discrete choice models. Al Ahmadi (2006) developed intercity mode choice models for Saudi Arabia. In this study he considered Multinomial logit model for the model development. Data collection was done through revealed preference surveys. The results indicated that in-vehicle travel time, out of pocket cost, number of family members travelling together, monthly income, travel distance, nationality of traveller, and number of cars owned by family played the major role in decision related to intercity mode choice. Khan (2007) estimated various nested logit models for different trip length and trip purpose using data from stated preference (SP) survey. A unique set of access modes for bus on bus way was generating containing hypothetical modes such as secure park and ride facilities and kiss and ride drop-off zones. He found that the travel behaviour forecasted for regional trip makers is considering different from that for local trip makers. In India, most of the modeling approaches are oriented towards the use of economic theory of Utility maximization. Many researchers have developed mode choice models based on principles of utility maximization (Chari 1978). Whereas disutility of minimization employed by Rao (1988). Parida (1994) has employed stated and revealed preference approaches for modeling home based work trips in Delhi. Subbarao et.al (1997) developed access mode choice model using ANN and compared the results with conventional Multinomial Logit model (MNL). Ravi Sekhar (1999) developed mode choice model by using ANN and MNL for Delhi data. In this study, data has been classified based on vehicle ownership. Ravi Sekhar et.al (2009) studied on applications of Neural Networks in mode choice modelling for second order metropolitan cities of India for this they have considered second order cities travel behavior data in the cities of Visakhapatnam and Nagpur.Ashalata et al. (2013) attempted a revealed preference study of mode choice for Thiruvanathpuram city using Multinomial logistic regression. The major modes included in the study were car, two wheeler and bus. The analysis highlighted 68

75 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi the fact that preference to car increases with age while the preference to use of two wheeler decreases in comparison to bus. 3.0 STDY AREA AND DATA COLLECTION 3.1 Study Area In this study commuter in Delhi was considered. Delhi has a population density of 11,297 per square km. Mobilising such dense population in a metropolitan which houses multiple offices, industries and manufacturing units is a marathon task. The city suffers with the problem of Congestion, Road fatalities and high levels of Pollution. The use of private vehicles (Drive alone cars and Two wheelers) is very familiar and plays a devious role in choking the networks of the city in peak and off peak hours. The city has been mainly divided four zones namely East, West, North and South Delhi, Figure 1 represents the study area. A total sample size of 5000 households interviews were collected. The numbers in the popup represents the number of samples collected from different zones. The survey questionnaire and method of data collection was briefly discussed in the subsequent sections. Figure 1: Study Area and Sample size of Data Collection from each zone 3.2 Questionnaire Design The questionnaire was comprised of four sections namely household information, person information, trip Information and vehicle Information. Household Information includes household size, number of earned persons in household, household income, monthly household travel expenditure and dwelling unit type. Personal information such as age, gender, education level, occupation and possession of driving license parameters was considered. Travel Distance (Home to work), travel time, access time, waiting time, transfer time, Parking time, egress time, travel cost and preferred mode of travel was pursued under trio information. Vehicle ownership, number and type of vehicles owned were captured considered under Vehicle information. Ranking type questions were also considered for modal serviceability attributes like cost, security, hygiene, privacy, travel time reliability, waiting time were to be ranked for public mode. 3.3 Travel Behavior Data Collection In the present study, household interview survey was conducted by CSIR- Central Road Research Institute through predesigned questionnaire. In all 5000 house hold sample were collected in Delhi. Stratified random sample was considered. In the first stage of sampling, blocks or clusters of colonies was recognized. In the second stage of sampling particular Households (HH) were identified and household members were interviewed. This type of sampling avoids any sort of biasness in the data collection procedure. The interviewers visited the pre- identified pockets and interviewed the household member. A sample size of 3000 survey responses was collected from South Delhi and 2000 samples were collected from the North, East and West Delhi. From the data, it was observed that the largest commuter share comes from the age group of 31 to 50 years with approximately 20%, 12%, 15% of them using drive alone car, two wheelers and bus respectively. The female commuters most preferred mode of travel is drive alone car which definitely provides higher security and privacy to women. The house hold income of less than per 69

76 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi month mostly uses two wheelers, buses and walk. The variables considered to influence the mode choice behaviour and available choice of mode are (as per the survey data) listed in Table 1. Traditionally such explanatory variables include household/personal, socio-demographic and trip information. Table 1: Explanatory variables Choice Variables considered for mode choice analysis Description of Model Input Variable Variable Type House hold Information Household size Continuous Number of vehicles in house hold Continuous Household income (Indian Rupees) Continuous Personal Information Age of traveler in years Continuous Gender of traveler 1:Male; 0:Female Education Level 1.Elementary; 2.Intermediate; 3:Diploma; 4:Graduate; 5: Postgraduate; 6:Doctrate; 7:Post Doctorate Type of employment 1:Student; 2:Govt.employee;3:PrivateSector Employee;4:Business Owner;5:Other Possession of Driver s License 1: Yes; 0:No Trip Information Trip Purpose 1: Work trip; 0: Non Work Trip Serviceability provided by Mode in use Continuous In Vehicle Travel time for MV (Minutes) Continuous Out of Vehicle Travel time for MV (Minutes) Continuous Travel cost ( Indian Rupees) Continuous Total Travel Time for NMV ( Minutes) Continuous Available Mode Choice Discrete Drive Alone Car (Private mode) Carpool (Shared mode) Two Wheeler (Private mode) Bus (Public mode) Metro (Public mode) Auto Rickshaw (IPT mode) Bicycle (Personal/ Non motorized mode) Walk (Non motorized mode) 70

77 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi 4.0 MODE CHOICE ANALYSIS 4.1 Development of Multinomial Logit Model Logit models are mainly three types, namely Multinomial Logit Model (MNL), Conditional logit model (CL) and Mixed Logit model (MXL). Logit models depending on whether the data are chooser-specific or choice-specific. MNL model has chooser specific data where coefficients vary over the choices. CL model has choice-specific data where the coefficients are equal for all choices. MXL model involves both types of data and coefficients. MNL model is widely used disaggregate mode choice model, it estimates the proportion of trip makers who choose available mode types based on given conditions or utility criteria. MNL model is often used to compare with other techniques, due to its ability in analyzing the trip maker behavior (Hensher et. al,2000). MNL model considered in this study to model choice behiour commuters in Delhi. The mathematical framework of logit models is based on the theory of utility maximization (Ben-Akiva and Lerman, 1985). Probability of an individual "i" selecting a mode "n", out of "M" number of total available modes, is given in equation (1). P in = e V in M e V im m=1 Eqn. (1) Where, Vin is the utility function of mode "n" for individual "i", Vim is utility function of any mode "m" in the choice set for an individual "i". Pin is the probability of individual "i" selecting mode "n". M is the total number of available travelling modes in the choice set for individual "i". However, the Logit model has certain drawbacks like requirement of large sample size and restriction on dependent variable to be of discrete dataset. 4.2 Results and Discussion of MNL Model In this study four different mode choice models were developed to evaluate the behaviour of commuter to choose the particular mode. Base Model (M1): Initially, base model (M1) was developed, this model consisted of basic travel parameters like travel time which includes In Vehicle Travel Time(IVTT), Out of Vehicle Travel Time(OVTT) and House hold income as an Alternative Specific Constant (ASC). The inclusion of House hold income in the model will reflect the biases that by each commuter will have for mode selection with respect to change in Income. Walk was taken as the reference mode. Model 2 (M2): This model consists of incremental improvements on the specification of base model (M1). Improvement in model was explored by addition and interaction of various travel parameters. The possession of Driving License (DL) was included as a dummy variable. Other variables like age, gender and education level was also incorporated in the model specification. Number of workers in household, trip purpose and modal service level expected by the commuters was also included. Model M2 shows great improvements in its log likelihood value. The value obtained at convergence shows a value of as compared to Model M1 value of Also by log likelihood ratio test M2 rejects M1 significantly. Model 3 (M 3): To get a better insight into the travel behaviour of motorized and non motorized mode commuters, the travel time was split into two types in model 3 The first part was the total travel time for Non Motorized mode (NMV) i.e. Bicycle and Walk and the second part was that of IVTT and OVTT for Motorized vehicles (MV). The estimated MNL model coefficients for each model were presented in Table 2 with 71

78 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi changes in their utility function specifications. The influence of explanatory parameter in each model was evaluated through t-static value, which is the ratio of the model coefficient to standard error estimate. This is presented in the brackets of Table 2. The t- static values higher than ± 1.95 at 5% level of significance have significant contribution in the model development. The detailed result and discussion are described in next section. Model 4 (M4): In Model M4 interaction amongst variables were tried for further betterment of model. OVTT of motorized vehicles was interacted with the logarithm of distance travelled. Trip length is a trip parameter that will bring forth the sensitivity of commuters with respect to travel distance. The model shows a slight improvement on employing this variable interaction. Like hood ratio test was performed for validating the overall goodness of fit of model. The likelihood ratio test statistic is twice the difference in log-likelihoods of the two models under consideration (-2(LLbase model LLestimated model). For example LL ratio tests of Model M2, Model M3, Model M4 w.r.t. Base Model M1. The log like hood value has improved for Model3 significantly after this breakup of travel time component. Model M4 was found to be the most significant one. So, the coefficients used in model 4 are more appropriate and further considered for evaluating Value of travel time. From Akaike Information Criteria (AIC) values it was observed that Model 4 has the least AIC value compared to rest of the models indicates that this model is the one with the least divergence from truth (but few parameters). The DA car mode greatly dominates the work trips, followed by carpool and two wheelers. Bus, auto rickshaw and bicycle have a negative influence on the work trip makers. Metro has a relatively little although positive attraction for work trips. In MV the IVTT and OVTT has almost the same influence on travel. But the value obtained for IVTT of MV is more significant than the OVTT. Travel cost also has a significant and negative influence on travel where increase in cost is observed as undesirable. But the TT for NMV has a greater (negative) influence on the walk and bicycle modes. The TT_NMV is also highly significant and cannot be ignored. It was observed from the coefficients of the household income that the utility of drive alone car and carpool increases with increasing income. The lower income group makes use of the transit and para transit facilities more than other modes. Travelers with higher Education level the utility of bus, two wheelers, auto rickshaw and bicycle is very low. The older age group is inclined towards walking or shared car drive. The younger age group prefers use of two wheelers, metro and auto rickshaws while drive alone car is most popular with the middle age group commuters. Gender wise, two wheelers, carpool, bus and bicycles is preferred more by male travelers while female counterparts prefer use of metro, auto rickshaws and drive alone cars. Household with more number of working persons are more inclined towards carpooling and use of bus. For work trips drive alone car is the most popular mode of travel while bicycle is the least preferred one. The household that have a higher budget for traveling prefer use of car (alone and shared), Two wheelers and metro. 72

79 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi Modal Parameters Table 2: Estimation of Model Coefficients of Various MNL models Base Model 1 (M1) IVTT (1.65) (2.42) OVTT (0.55) (2.67) Model 2(M2) Model 3 (M3) Model4 (M4) TT_NMV (-6.17) (-7.74) IVTT_MV (-2.09) (-2.85) OVTT_MV (-0.08) OVTT_MV/log(Distance) (-1.52) Travel Cost (11.62) (7.38) (3.86) (3.97) Alternative Specific Constant (ASC) Drive Alone Car (0.24) (-3.23) (-3.43) (-3.18) Car Pool (23.75) (-7.9) (-8.63) (-8.55) Two-wheeler (9.45) (-0.12).172 (0.02) 0.46 (0.13) Bus (17.64) (1.9) (1.28) 1.51 (1.62) Metro (23.06).221 (-.99) (-1.07) (-0.85) Auto-Rickshaw (22.45) (0.63) (0.74) 1.99 (0.92) Bicycle 0.0 (1.92) (2.98) (3.06) 9.33 (3.09) Modal Serviceability Drive Alone Car.0018 (.12).001 (0.26) (0.30) Car Pool.1030 (3.51).103 (3.81) (3.75) Two-wheeler.0191 (1.28).019 (1.58) (1.56) Bus.0669 (4.80).066 (5.80) (5.86) Metro.0191 (0.92).019 (0.94) (0.94) Auto-Rickshaw.0342 (1.64).034 (1.84) (1.89) Bicycle.0173 (1.42).017 (1.59) (1.62) HH Income Drive Alone Car (0.23).658 (-2.52) (-2.83) (-2.84) Car Pool.8499 (23.75).650 (0.43) (0.23) (0.19) Two-wheeler (6.37) (-3.63) (-4.03) (-4.06) Bus.1401 (1.27) (-2.33) (-2.65) (-2.70) Metro (1.76) (-2.89) (-3.09) (-3.11) Auto-Rickshaw.6474 (22.06).126 (0.12) 0.0 (0.55) (0.52) Bicycle (3.65) (-1.85) (-1.95) (-1.98) Gender Drive Alone Car 8617 (2.63).8617 (2.99) (2.99) Car Pool (2.42) (2.67) (2.62) Two-wheeler (-4.53) (-4.03) (5.91) Bus (-2.45) (-2.65) (7.21) Metro 6334 (-2.90).6334 (-3.09) (1.82) Auto-Rickshaw 5931 (0.21).5931 (0.55) (1.60) Bicycle (-1.35) (-1.95) (1.96) Trip purpose Drive Alone Car (9.31) (9.28) (9.27) Car Pool (2.01) (1.88) (1.83) Two-wheeler.8445 (2.89).8449 (2.68) (2.64) Bus (-2.93) (-2.53) (-2.55) 73

80 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi Modal Parameters Base Model 1 (M1) Model 2(M2) Model 3 (M3) Model4 (M4) Metro (0.91).4443 (0.79) (0.77) Auto-Rickshaw (-2.01) (-1.97) (-1.95) Bicycle (-1.62) (-1.20) (-1.21) LL at Zero LL at Constants LL at Convergence AIC Likelihood Ratio Test NA Prediction Accuracy 52% 70% 72% 74% 5.0 ESTIMATION OF VALUE OF TIME Value of travel time (VOT) plays a crucial role in the cost benefit analysis in transport planning process. It quantifies the importance of time with respect to cost which is employed in economic evaluation of travel time saving. The most standard procedure suggests Value of Time as a trade off ratio between coefficient of travel time and travel cost. VOT is equal to the ratio between the derivative of utility with respect to time and the derivative of utility with respect to cost, mathematically expressed in equation (2) VoT = Vi Timei Eqn. (2) Vi Costi For utility functions that are interacted with other variables the VOT formulation changes slightly. This is expressed mathematically in equation (3) & (4). Vi Timei Log(Distance) VoT IVTT = Eqn(3) VoT OVTT Vi Vi Cost Timei Log(Distance) + βivtt = Eqn(4) Vi Cost Where, Log (Distance) is the Log (Trip distance) and β IVTT is the coefficient of IVTT obtained by model estimation. Value of In Vehicle Travel time as computed using the MNL model (w.r.t. Trip length) is presented in Table 3 row one. The value of travel time for In vehicle travel time for MV is obtained as 95( /Hr.). Value of Out of Vehicle travel time is highest for shorter trip and decreases as the trip length increases which imply that commuters are more sensitive to waiting time, parking time, access and egress time for shorter trips. When the trip length increases travelers are concerned more with the In Vehicle travel time and less with Out of Vehicle travel time. Also the travelers in non motorized modes are expected to be more sensitive to travel times as walking and bicycling are physically more demanding than the motorized modes (Koppleman and Bhat, 2006). The value of Travel time for non motorized modes is in accordance to our expectation and are very high ( 451/hr) compared to value of travel times of motorized modes. But in real case scenario not every individual values time equally. It is a matter of taste variations and personal preferences. To account for this heterogeneity, VOT should be quantified for different segments of the population under consideration based upon the attributes of involved society and the characteristics of the trip. Segmentation based on a) Gender and b) Trip Purpose has been done to investigate how it affects the VOT for 74

81 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi different situations. The Table 3 represents the VOT for the above mentioned stratifications. Heterogeneity in the population is clearly indicated by the VOT calculated for different segments. The gender classification of population depicts results that exhibit large heterogeneity in perception of travel time among male and female travelers. Male travelers are more sensitive towards the travel time. They perceive both the IVTT and OVTT for the trip made more critically than their female counterparts. But what is more pertinent is the high value of non motorized travel time. Female commuters perceive this time much more critically (approximately 100 times IVTT of MV). It subtly brings forth the safety aspect of NM modes of travel where walking and bicycle has been considered. The social security of female commuters plays a major factor in this aspect as walking to work from home (and back) has very high chances of eve teasing and other unwanted hassles that female commuters face every day. Value of IVTT of MV MNL Model (Pooled Model) 95/hr 122/hr Segmentation based on: Gender Male 127/hr Comparing the Work and Non work trips of travel; work trips have a very high value of travel times as expected. A work trip IVTT is valued 5 times more than IVTT of non work trips. During work trips the Out of Vehicle travel time spent for waiting, transfer and parking are all very crucial and the commuters seem to be very sensitive towards it. It implicitly suggests that the service level of operating modes affects the travelers more than the explicit factors in making their mode choice decision. Even the NM modes (i.e Walk and bicycle) place a very high value of their travel time (443 /Hr). This in fact indicates the plausible cause why the NMV have become so unpopular in making trips. Non work trips show great variation with IVTT being valued at just 24 /Hr while OVTT is valued almost 3 times higher (73 /Hr.) This result asserts that for non work trips, commuters are very critical about comfort and convenience and every minute spend in waiting, walking or parking is deemed as very crucial. Table 3 Value of Time for different Segments based on Trip length 5 Km trip 10 Km trip 15 Km trip Value Value Value of Value Value of Value Value of of TT of TT OVTT of IVTT OVTT of IVTT OVTT of of of MV of MV of MV of MV of MV NMV NMV 451/hr 156/hr 464/hr Female 3/hr 35/hr 366/hr Segmentation based on: Trip Purpose Work trip 122/hr Non trip work 132/hr 443/hr 24/hr 73/hr 222/hr 95/hr 85/hr 451/hr 127/hr 110/hr 464/hr 3/hr 24/hr 366/hr 122/hr 92/hr 443/hr 24/hr 51/hr 222/hr 95/hr 73/hr Value of TT of NMV 451/hr 127/hr 92/hr 464/hr 3/hr 20/hr 366/hr 122/hr 78/hr 443/hr 24/hr 43/hr 222/hr 6.0 SIGNIFICANT FINDINGS This study focused on the mode choice analysis of Delhi which is subjected to heavy congestion and pollution due to high number 75 of private vehicles plying on the roads. The data collection was done through a household mode choice models were developed and various explanatory variables were

82 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi incorporated to improve the goodness survey in Delhi. A large Household survey sample of 5000 responses was collected and discrete disaggregate Multinomial logit model was considered for carrying out mode choice analysis. Four different Information Criterion value it can be concluded that M4 has better predictability in modelling mode choice behaviour. Therefore the coefficients obtained from Model M4 were employed for investigating and estimating the value of travel time. the fit and interpretability of the MNL model. Model M4, from both log likelihood value and Akaike Travel time is a valuable nonrenewable resource and people devote a great deal of their time in travelling and thus it forms a strong basis for evaluation of any transport system. Travel time savings is often the principal benefit of a transportation project. The findings of this study be used in congestion relief projects are justified primarily by the reduction in travel time they will bring about. Travel time savings can also lead to reductions in vehicle operating costs. Thus the VOT estimated in the study can prove useful in case when new policies and infrastructure are appended to the transportation system. The VOT will render useful in the cost benefit analysis. Further, the following are the significant conclusions drawn from the results. The coefficients obtained in the model4 (M4) indicate that affluent and higher income household prefers Drive alone Car and Carpool as their major mode choice decision which is also indicative of a lavish lifestyle. The interaction of variables in model 4 (M4) gives a better result with increases predictability of 74% compared to M1, M2 and M3 which have a prediction accuracy of 52%, 70% and 72% respectively. The strata of society with higher level of education are least inclined towards use of Public and Inter Para Transit facilities. Female commuters prefer Drive Alone car, Metro and Auto Rickshaw than other available modes. It is found that Value of Travel time for NMV is 5 times the Value of In Vehicle Travel Time for MV which is expected as each minute spend in walking and cycling is more demanding than the same time spend in travelling through a motorized vehicle REFERENCES 1. Adams, W.T. (1959), Factors Influencing Mass Transit and Automobile Travel in Urban Areas, Public Transport, (30), pp Ashalata, R., MAnju, V.S. and Zacharia, A. B.(2013), Mode Choice Behaviour of Commuters in Thiruvananthapuram City, Journal of Transportation Engineering, American Society of Civil Engineers, Volume 139, Issue 5, pg Abdel-Aty, M. and Abdelwahab, H. (2001), Calibration of nested mode choice model for Florida, Final research report, University of central Florida. 4. Al-Ahmadi, H.M. (2006), Development of Intercity Mode Choice Models for Saudi Arabia, JKAU: Eng.Sci, Vol. 17 No. 1, pp Ben-Akiva, M. E. and Lerman, S. R., (1985), Discrete Choice Analysis: Theory and Application to Travel Demand, The MIT Press, Cambridge, Massachusetts, the USA. 6. Bhat, C.R., and R. Sardesai (2006), "The Impact of Stop-Making and Travel Time Reliability on Commute Mode Choice," Transportation Research Part B, Vol. 40, No. 9, pp Bhat, C.R. (1995), "A Heteroscedastic Extreme Value Model of Intercity Mode Choice", Transportation Research Part B, Vol. 29, No. 6, pp Bhat, C. R. and Pulugurta, V. (1998). A Comparison of Two Alternative Behavioral Mechanisms for Car Ownership Decisions. 76

83 Modeling Mode Choice Behaviour and Estimating Value of Travel Time of Commuters in Delhi Transportation Research Part B, 32(1), Gebeyehu, M. and Takano S,(2007), Diagnostic Evaluation of Public Transportation Mode Choice in Addis Ababa, Journal of Public Transportation, Vol. 10, No Gliebe, J.P., F.S. Koppelman and A. Ziliaskopoulos (1998) Route choice using a paired combinatorial logit model, prepared for presentation at the 78th meeting of the Transportation Research Board,Washington, D.C., January Hensher, D. A. and T. Ton (2000). TRESIS: A transportation, land use and environmental strategy impact simulator for urban areas. Transportation 29(4): Khan,O. (2007), Modelling Passenger Mode Choice Behavior Using Computer Aided Stated Preference Data, P.H.D. thesis, Queensland University of technology 13. McDonald, N.C.(2008). Children s mode choice for the school trip: the role of distance and school location in walking to school. Transportation Vol. 35 No.2, 2008, pp McFadden, D. (1978), Modelling the choice of residential location, in A. Karlquist, ed., Spatial Interaction Theory and Planning Models, North Holland, Amsterdam, pp Mukala, P.K and Chunchu, M.(2011), Mode choice modelling for intercity transportation in India:A case of Guwahati to five metro cities, International Journal of Earth Sciences and Engineering. Volume 04, No 06 SPL, pp Parida,M.,(1994) Mode Choice Analyis Based on Stated and revealed Preferences for Home Based Work Trips in Delhi, PHD Thesis Department of Civil Engineering, IIT Roorkee, Roorkee,India. 17. Proussaloglou, K., F. S. Koppelman, The Choice of Air Carrier, Flight, and Fare Class.Journal of Air Transport Management 5 (4), Provisional Population Totals Paper 1 of 2011 : NCT of Delhi, Chapter-2, Data and Major Trends,pp Ravi Sekhar Ch. (1999) Mode choice analysis using Neural Network, Department of Civil Engineering, Master Dissertation, IIT Roorkee, Roorkee, India. 20. Ravi SekharCh.,Madhu,E, Durai,B.K and Gangopadyayay. S Applications of Neural Networks in Mode Choice Modelling for Second Order Metropolitan Cities of India, Proceedings of the Eastern Asia Society for Transportation Studies, Vol.7, Subba Rao, P. V., Dhingra, S. L., Sikdar, P. K. and Krishna Rao, K. V. (1997) Access mode choice analysis using artificial neural networks, Proceedings of the Conference on Trends and Techniques of Transportation, REC Warangal, India. 1997, Sidharthan,R., Bhat,C.,Pendyala,R. and Goulias, K.,(2011) A Model Of Children s School Travel Mode Choice Behavior Accounting For Spatial And Social Interaction Effects, Transportation Research Record: Journal of the Transportation Research Board, vol. 2213,pp Wen, C. and F.S. Koppelman (1999) An Integrated Model System of Stop Generation and Tour Formation for the Analysis of Activity and Travel Patterns, forthcoming, Transportation Research Record 24. Xie, C., Lu, J., Parkany, E. (2003) Work Travel Mode Choice Modeling Using Data Mining: Decision Trees And Neural Networks, Transportation Research Record: Journal of the Transportation Research Board, No

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85 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 SELECTION OF ROUNDABOUT ENTRY CAPACITY MODEL FOR INDIAN CONDITION Abdullah Ahmad*, Srinath Mahesh** and Rajat Rastogi*** Abstract: Evaluating the capacity of roundabout is an important element in the planning and design of such facilities. An empirical approach using regression analysis was used to develop a roundabout entry-capacity model for Indian conditions. US model gave results close to that of field entry capacity model and hence US model was calibrated for Indian condition. Keywords: Roundabouts, critical gap, entry flow, circulating flow, entry capacity models 1.0 INTRODUCTION A roundabout is an unsignalized intersection with a circulating roadway and connecting legs. It is a one-way circular intersection without any traffic signal equipment in which traffic flows around a central island, clockwise for left-side driving and anti-clockwise for right-side driving. It operates with yield control at the entry points, and gives priority to vehicles within the roundabout. There is no specific way to distinguish roundabouts from traffic circles or rotaries. In the United States, these circular intersections are classified into three categories such as rotaries (traffic circles), neighborhood traffic circles and roundabouts (FHWA 2000). The fundamental difference is in their design philosophies. Roundabouts control and maintain low speeds for entering and circulating traffic. This is achieved by small diameters and low-speed entry geometry. By contrast, rotary geometry encourages high-speed merging and weaving, made possible by larger diameters and large high-speed entry radii. The geometric design elements of roundabouts allow only slow speeds therefore creating safer driving conditions. A considerable number of roundabouts have been installed in India. Roundabouts have been used worldwide as an efficient intersection control type to improve safety and operational efficiency. Evaluation of roundabout capacity is very important since it is directly related to delay, level of service, accident, operation cost, and environmental issues. For estimating entry capacity, the gap acceptance approach and the empirical approach are used now-a-days. The entry capacity is considered as a function of circulating flow as the circulating flow decreases, the entry capacity increases due to higher opportunities available for entering the circulation area. The gap acceptance approach estimates the entry capacity using critical gap and follow-up time parameters. The empirical approach estimates the entry capacity based on the observed capacity of the existing roundabouts which have been installed in early days. Although the empirical models can best reflect local traffic conditions, they cannot be applied to other locations as such. Some of the recent works in this area are as under i. Çalişkanelli et al. (2009) applied regression analysis method to compare the capacity models. The data was collected at four approaches of four multi-lane and seven approaches of five single-lane traffic circles in Izmir, Turkey. They found that the method of critical gap acceptance gave * Research Scholar, Department of Civil Engineering, Indian Institute of Technology, ROORKEE INDIA, abd.zubairi@gmail.com ** M.Tech Student, Department of Civil Engineering, Indian Institute of Technology, ROORKEE INDIA, srinath.nda@gmail.com *** Associate Professor of Civil Engineering, Indian Institute of Technology, ROORKEE INDIA, rajatfce@iitr.ernet.in

86 Selection of Roundabout Entry Capacity Model for Indian Condition ii. iii. iv. more accurate results than the other models. Mazzella et al. (2011) considered a geostatistical approach to establish the relationship between entry capacity and circulating flow. It emphasized that the relationship between entry capacity and circulating flow cannot be expressed by one trend only but by two or three trends. Chandra and Rastogi (2012) proposed a method to determine the entry capacity of a roundabout in India. Data was collected at four roundabouts in the suburban area of Chandigarh city and analyzed using five different methods of determining the entry capacity. The proposed method gave the capacity, quite comparable to the German entry capacity model. Indian model (IRC - 65) gave the highest capacity amongst the methods being considered i.e. UK, Swiss, HCM and German model. However, Indian model is based on the capacity of weaving section which can accommodate the least traffic. Among other four methods, UK model gave the highest entry capacity and the US model gave the lowest capacity. Dahl and Lee (2012) found that the observed entry capacity was lower for the roundabout with a higher truck percentage. As truck percentage increased, the critical gap and the follow-up time for the roundabout increased, thus resulting in lower entry capacities. The results showed that the capacity decreased as truck percentage increased, but the amount of capacity reduction was less at higher circulating flows. In India, no comprehensive study is being carried out to develop a model to estimate the entry capacity of the roundabout. Few attempts have been made to analyze the traffic flow and estimate the entry capacity of the roundabout. In this study, an effort is being made to develop a regression model for estimating roundabout entry capacity as a function of the circulating traffic. The empirical approach, using regression analysis, is used with this purpose. The study is taken up as a part of development of Indo-HCM. The objective of this paper is to present and compare different types of entry capacity models based on gap acceptance and regression, and their application to the settings of roundabouts in the context of developing nations like India. 2.0 REVIEW OF ENTRY CAPACITY MODELS 2.1.French Model(Guichet 1997) The French formula for the estimation of entry capacity (pcu/h), is based on the regression analysis, and is given by equation (1). This method considers the disturbing flow in front of entry, follow up time and roundabout geometries. The method is somewhat complicated as the number of variables and associated estimation formula are more. C B*Qd 3600 C = A.e (1) Where, W e A = T f 3.5 (2) T f = follow-up time (sec) W e = entry width (m) C B = coefficient that is for urban areas and for rural areas Q d = disturbing flow in front of the entry (pcu/h) Q = Q.k. 1- Q + Q.k + Q.k (3) Q u = exiting flow (pcu/h) Q c = Q ci + Q ce = circulating flow (pcu/h) Q ci = circulating flowon the far lane (pcu/h) Q ce = circulating flowon the near lane (close to the entry) (pcu/h) u d u a ci ti ce te Q u + Qc R L R + W L max for L <L max K a = (4) 0 for other cases R= central island radius (m) 80

87 Selection of Roundabout Entry Capacity Model for Indian Condition W = circulating roadway width (m) L= splitter island width (m) W L = 4.55 R + (5) 2 max 160 k ti = Min or 1 W*(R + W) (6) 2 (W -8) R k te = Min 1- or 1 W R + W (7) 2.2.Jordan Model (Al-Masaeid and Faddah 1997) The model was developed based on analysis using data from ten roundabouts. The range of entry width and diameter of selected roundabouts were 5-18 meter and 8-77 meter respectively. The entry capacity model was defined as a function of circulating traffic flow, circulating width, entry width, diameter of the central island, and distance between the entry and its near exit. It is given by equation (8) Q c EW RW Q e =168.2 D S e e (8) Where, Q e = entry capacity (pcu/hr) Q c = circulating traffic flow (pcu/hr) D = central island diameter (m) S = distance between the entry and nearside exit (m) EW = entry width (m) RW = circulating roadway width (m) 2.3.German Model (Brilon and Wu 2006) The model for the estimation of roundabout entry capacity was based on an idea from Tanner (1967) as cited by Mauro and Branco (2010). This is given by equation (9). The method considers circulating flow, geometry of the roundabouts and traffic flow micro characteristics like gap, follow-up time and headway. n c.q c / 3600 ne Qc Tf C = * *exp - * Tc - - nc T f (9) Where: C = entry capacity (pcu/h) Q c = circulating flow in front of the entry (pcu/h) n c = number of circular lanes n e = number of lanes in the subject entry T c = critical gap (sec) T f = follow-up time (sec) = minimum headway between the vehicles circulating in the circle 2.4.US Model (HCM 2010) This is an exponential model of entry capacity for roundabouts. It is a combination of simple, lane-based regression (exponential) and gap-acceptance model. The roundabout capacity model for an entry lane is expressed as given by equation (10). e B*V c C = A*e (10) Where, 3600 A= (11) t t B= c f 0.5* t 3600 f V c = conflicting flow rate in pcu/h t f = follow-up time (s), t c = critical gap (s) 3.0 DATA COLLECTION AND EXTRACTION (12) For any traffic study, data collection is extremely important and it is to be carried out very carefully. The accuracy and care with which the data collection is being carried out in turn greatly affects the results. Therefore, video recording technique was used to collect the data at a roundabout. The use of a video camera allows the collection of data with minimum number of personnel and the video tapes can be viewed several times to obtain the 81

88 Selection of Roundabout Entry Capacity Model for Indian Condition multiple desired information. Study was carried out for a selected roundabout in Chandigarh, India. The video camera was mounted on a stand and was placed on the roof of a building located near the roundabout. Recording was done for about 4 hours which included morning peak period. The selected roundabout was located in urban area and a) Entry Width = 8.5 meter b) Exit Width = 8.5 meter c) Approach Width = 7.5 m d) Departure Width = 7.5 m e) Circulating roadway width = 7 m (2- lanes) there was no interference from pedestrians. There were no parking and bus bays nearby and roundabout was sufficiently away from upstream and downstream signals. A snapshot of selected roundabout is shown as Error! Reference source not found.. The common geometric features of the roundabout were: f) Weaving length = 33 m g) Central Island Diameter = 37 m h) Central Island Perimeter = 115 m i) Splitter Island = 3.5 m Figure 1: Snapshot of selected roundabout in Chandigarh In India, there is a mixed traffic condition and during peak flows, lane discipline is not followed. As entry driver tries to get space to keep moving the gap acceptance behavior becomes quite complex. Consequently, data extraction for gap acceptance or rejection process is a challenging task. The video was played to extract the desired information. Data on entry flow, circulating flow, accepted gap and rejected gap by an entering vehicle were recorded. Gap data were extracted with an accuracy of 0.01 second. The categories of vehicles found in urban area such as motorized two-wheelers (2W), motorized three-wheelers (3W), cars (CAR) and heavy vehicles (HV) were considered for the analysis. The average composition of traffic stream at entry flow and circulating flow are shown in Table 1. The entry traffic flow on an approach while having a stable queue and the corresponding circulating traffic flow were extracted for period of queue dissipation ranging from 45 82

89 Selection of Roundabout Entry Capacity Model for Indian Condition seconds to 4 minutes and were extrapolated to the equivalent hourly flow. Table 1: Composition of different types of vehicles (%) Flow Conditions HV 3W Car TW Total Entry flow Circulating flow Where, q e = entry flow (pcu/hr), e = base of natural logarithm, and 4.0 DEVELOPMENT OF ENTRY CAPACITY MODEL For estimating the entry capacity, the circulating traffic flow and entry traffic flow data were collected during periods of continuous and stable queuing at the entry leg of the roundabout. The entry capacity of the roundabout will be the observed entry traffic flow with a stable queue and a continuous stream of vehicles with respect to the circulating traffic flow (Al-Masaeid and Faddah 1997). Circulating traffic flow and entry capacity are expressed in passenger car units (pcu) to account for two wheelers, three wheelers and heavy vehicles. For conversion into pcu, two wheelers are 0.75 pcu, three wheelers are 1.0 pcu and heavy vehicles are assessed as 2.8 pcu(irc-65, 1976). A scatterplot of entry capacity and circulating traffic flow at roundabout is shown in Figure 2Error! Reference source not found..the relationship between entry capacity and circulating traffic flow was investigated and regression analysis was carried out to determine the best fitted equation by using entry capacity and circulating traffic data. It is given in equation (13). R square value for the exponential model was higher than the R square value for linear and other models. The relationship has been in line with the reported literature (Al-Masaeid and Faddah 1997; HCM 2000, 2010). qe *q 4752*e c (R 2 = ) (13) Figure 2: Entry flow versus circulating flow q c = circulating traffic flow (pcu/hr) 5.0 ANALYSIS OF RESULTS 5.1 Critical Gap and Follow-up Time Estimation The critical gap is also extracted in this case study for the comparison of the existing entry capacity model as already discussed above. Miller (1972) compared different methods of critical gap estimation by using simple gap acceptance model. The study found that Maximum Likelihood technique and Ashworth method gave acceptable results. Maximum Likelihood technique was also recommended by Troutbeck (1992). Brilon et al. (1999) concluded that the Maximum likelihood method and Hewitt s method give the best results. NingWu (2012) proposed a method based on equilibrium of probabilities for estimation of critical gap at unsignalised intersection. Troutbeck (2014) compared the Ning Wu method and Maximum Likelihood Method. It was found that Maximum Likelihood method was slightly better than Ning Wu method. Among these methods, only Maximum Likelihood Method proves to be the most accurate and reliable. This method requires data on both rejected gaps and accepted gap by a vehicle. It utilizes the data in pairs of highest rejected gap and next accepted gap. Consequently, the critical gap 83

90 Selection of Roundabout Entry Capacity Model for Indian Condition values are estimated using the maximum likelihood method for motorized twowheelers, motorized three-wheelers, cars and heavy vehicles separately. These are given in Error! Reference source not found.. Then the critical gaps for the entire entry flow were calculated as a volume-weighted average of the critical gap for two- wheelers, motorized three-wheelers, cars and heavy vehicles (Dahl and Lee 2012). Table 2: Critical gaps estimated by maximum likelihood method Critical Gaps (s) TW 3W CAR HV Weighted Average Critical Gap (s) Follow-up times represent the process by which multiple vehicles that are queued at an approach can enter the roundabout. In this study, the extraction of follow up time was very difficult. Therefore, the follow-up time was taken as 0.6 times of the critical gap as reported in literature (Brilon 1988; Hagring et al. 2003; Tian et al. 2000). The extracted critical gap and follow-up time for Indian condition were used in French, German and HCM (2010) entry capacity model. 5.2 Comparison of Capacity Models The field entry capacity model was compared with the already presented entry capacity models. Error! Reference source not found. shows the comparison between the field entry capacity model and the other models. Compared with French, Jordan, German and the US entry capacity models for roundabouts, the developed field entry capacity model provided comparable estimates with German capacity model only for 900 to 1400 pcu/h of circulating traffic. The field entry capacity model gave the highest capacity amongst all the methods i.e. French, Jordan, German and the US model for circulating traffic higher than 1100 pcu/h. The US model gave higher capacity than French and Jordan model for larger than 1100 pcu/h of circulating traffic. Jordan model gave the lowest entry capacity amongst all the methods. For circulating volume less than 1100 pcu/h, German and French model predicted higher capacity than the other existing methods. The trend followed by field data model in India was comparable to that by the US model and Jordan model. Field data model was higher than the other two models. It signifies that under mixed traffic conditions, the entry capacity is higher than that under uniform traffic condition. Figure 3: Comparison of entry capacity models Regression analysis has been done for finding out the best capacity model among the existing entry capacity models for Indian condition. Error! Reference source not found. to Error! Reference source not found. show the field entry capacity model versus existing entry capacity models to find the best toning of the existing entry capacity models with the field model. Relations between existing entry capacity models and field entry capacity model have been developed. The linear relationships have been taken into account rather than exponential or other models for matching the style of the existing entry capacity models with the field model. The matching of French, Jordan and German models with field entry capacity model gave low R 2 value as compared to the US model. The best matching with the field entry capacity model came out with the US model for which R 2 value was Based on the R 2 value, the US model complemented with the field entry capacity model and can be 84

91 Selection of Roundabout Entry Capacity Model for Indian Condition calibrated easily for Indian condition as given below: US Model Field Model * Field Model (14) 1.478* USModel (15) For practical application, the US model for estimating entry capacity is simplified as given below in equation (16) for Indian condition. e B*V c C =1.478*A*e (16) Where, 3600 A= t (17) t B= c f 0.5* t 3600 f (18) V c = conflicting flow rate in pcu/h t f = follow-up time for Indian traffic condition (s), t c = critical gap for Indian traffic condition (s) Figure 7: French model v/s Field model Figure 6: Jordan model v/s Field model Figure 8: German model v/s Field model Figure 9: The US model v/s Field model 85

92 Selection of Roundabout Entry Capacity Model for Indian Condition 6.0 CONCLUSIONS The following conclusions are drawn from the study: i. An empirical approach using regression analysis was used to develop a roundabout entry-capacity model for Indian conditions. The entry capacity of an approach of a roundabout is dependent on the circulating flow in front of that approach. As the circulating traffic increases, the entry capacity decreases and their relationship is found to be of negative exponential nature. ii. The circulating flow versus field entry capacity charts were prepared for five models. The field entry capacity model gave the highest capacity amongst all the methods i.e. French, Jordan, German and the US model for circulating traffic higher than 1100 pcu/h. Jordan model gave the lowest entry capacity amongst all the methods. iii. By using regression analysis, the field entry capacity model was compared with other entry capacity models and it was found that only US model gave best toning to that of field entry capacity model for Indian condition. iv. The US model for estimating the entry capacity is simple as compared to other models since the number of variables and associated estimation formula are less. Henceforth, an adjustment factor was applied to calibrate US entry capacity model for Indian condition. v. The critical gap and follow-up time values recommended in HCM (2010) are not applicable to Indian conditions where smaller sized vehicles accept a much lower gap and are able to force their entry into the circulating roadway of the roundabout. The present study can be extended by considering the following aspects: i. To develop capacity charts for roundabouts having different geometric ii. condition like entry width, circulating roadway width and diameter of central island. To estimate the dynamic passenger car unit (pcu) values for all types of vehicles which can be applied in the case of roundabouts. REFERENCES 1. Al-Masaeid, H., and Faddah, M. (1997). Capacity of roundabouts in Jordan. Transportation Research Record: Journal of the Transportation Research Board, No. 1572, Brilon, W. (1988). Recent developments in calculation methods for unsignalized intersections in West Germany. Intersections without Traffic Signals, Springer Berlin Heidelberg, Brilon, W., Koenig, R., and Troutbeck, R. J. (1999). Useful estimation procedures for critical gaps. Transportation Research Part A: Policy and Practice, 33(3-4), Brilon, W., and Wu, N. (2006). Merkblatt für die Anlage von Kreisverke- hren [Guideline for the design of roundabouts]. FGSV Verlag Gmbh, Cologne. 5. Çalişkanelli, P., Özuysal, M., Tanyel, S., and Yayla, N. (2009). Comparison of different capacity models for traffic circles. Transport, 24(4), Chandra, S., and Rastogi, R. (2012). Mixed traffic flow analysis on roundabouts. Journal of the Indian Roads Congress, 73(1), Dahl, J., and Lee, C. (2012). Empirical estimation of capacity for roundabouts using adjusted gap-acceptance parameters for trucks. Transportation Research Record: Journal of the Transportation Research Board, No. 2312, FHWA. (2000). Roundabouts: An informational guide. Federal Highway Administration, Washington D.C. 9. Guichet, B. (1997). Roundabouts in France: Development, safety, design, and capacity. 3rd International Symposium 86

93 Selection of Roundabout Entry Capacity Model for Indian Condition on Intersections Without Traffic Signals, Portland, Oregon, USA, July 21-23, Hagring, O., Rouphail, N. M., and Sørensen, H. A. (2003). Comparison of capacity models for two-lane roundabouts. Transportation Research Record: Journal of the Transportation Research Board, No. 1852, HCM. (2000). Highway capacity manual Transportation Research Board, National Research Council. 12. HCM. (2010). Highway capacity manual Transportation Research Board, National Research Council. 13. IRC-65. (1976). Recommendation practice for traffic rotaries. Indian Roads Congress, New Delhi, India. 14. Mauro, R., and Branco, F. (2010). Comparative analysis of compact multilane roundabouts and turboroundabouts. Journal of Transportation Engineering, 136(4), Mazzella, A., Piras, C., and Pinna, F. (2011). Use of Kriging technique to study roundabout performance. Transportation Research Record: Journal of the Transportation Research Board, No. 2241, Miller, A. (1972). Nine estimators of gapacceptance parameters. 5th International Symposium on the Theory of Traffic Flow and Transportation, Newell, G. F. (ed), American Elsevier Publ. Co, Inc., New York. 17. Tanner, J. (1967). The capacity of an uncontrolled intersection. Biometrika, 54(3), Tian, Z., Troutbeck, R., and Kyte, M. (2000). A further investigation on critical gap and follow-up time. Transportation Research Circular E-C018: 4th International Symposium on Highway Capacity, Maui, Hawaii, June 27 July 1, Troutbeck, R. (1992). Estimating the critical acceptance gap from traffic movements. Physical Infrastructure Centre Research Report 92-5, Queensland University of Technology, Brisbane, Australia. 20. Troutbeck, R. (2014). Estimating the mean critical gap. Transportation Research Board 93rd Annual Meeting, Washington, D.C., January Wu, N. (2012). Equilibrium of probabilities for estimating distribution function of critical gaps at unsignalized intersections. Transportation Research Record: Journal of the Transportation Research Board, No. 2286,

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95 URBAN TRANSPORT JOURNAL Vol.13 No.1, Sept 2014 FRAMEWORK FOR DEVELOPMENT OF ADVANCED TRAVELER INFORMATION SYSTEM: A CASE STUDY FOR CHANDIGARH CITY Bhupendra Singh*, Ankit Gupta**, Sanjeev Suman*** Abstract: Intelligent Transportation System (ITS) is an area of wide research in developed countries and a lots of research work has been done in this area in the last two decades. There are many sub branches of ITS out of which one of the most widely used worldwide is Advanced Traveler Information System (ATIS), which can provide the information regarding the basic facilities to a traveler in a city. Geographical information system (GIS) is a powerful tool for storage, graphical representation and analysis of information of large data which makes it very useful for the development of ATIS. In this paper, a comprehensive framework comprising of system architecture, development methodology, and salient features of a GIS based ATIS for Chandigarh City, India has been discussed. The suggested system is able to provide the information about the basic facilities of the city and help the users in planning and decision making about their trips by providing shortest routes, nearest facilities and bus routes. This system can be stationed at public places such as in KIOSK and used in personal computers at homes and offices. Keywords: Advanced Traveler Information System, Intelligent Transportation System, geographical information system, ArcGIS. 1.0 INTRODUCTION Last two decades have seen a lot of development in the field of transportation infrastructure even then various traffic problems are increasing day by day. This is mainly due to the increase in number of vehicles. Almost every country of the world whether developing or developed, facing problems in the management of transportation facilities (Singh and Gupta, 2013). To solve these problems the focus of countries is shifting from the infrastructure development to the optimum and best use of the already constructed facilities and in this direction ITS proves to be very useful. Intelligent Transportation system is being utilized all over the world to manage and solve different traffic and transportation problems. ITS is an integrated system that implements a broad range of communication, control, vehicle sensing and electronics technologies to help in monitoring and managing traffic flow, reducing congestion, providing optimum routes to travelers, enhancing productivity of the system, and saving lives, time and money. ITS aims to improve the safety and efficiency of the transportation system. ITS is a very big area of study in itself containing lots of subsidiary branches based on the use of them in different traffic management fields, out of which most important and widely used all over the world to solve the traffic and transportation problem are as follows: Advanced Traveler Information System (ATIS) Advanced Traffic Management System (ATMS) Advanced Public Transportation System (APTS), and Emergency Management System (EMC). Advanced Traveler Information System (ATIS) implements a wide range of technologies, such as internet web sites, * Post Graduate Student, Civil Engineering Department, National Institute of Technology (NIT) Hamirpur, bhupendra2211@gmail.com ** Assistant Professor, Civil Engineering Department, NIT,Hamirpur, Himachal Pradesh, anki_ce11@yahoo.co.in ** Assistant Professor, Civil Engineering Department, College of Technology, G. B. Pant University of Agriculture & Technology, Pantnagar (U. S. Nagar), Uttarakhand sanjeevsuman @gmail.com

96 Framework For Development Of Advanced Traveler Information System: A Case Study For Chandigarh City telephones, cellular phones, television, radio, etc. to assist travelers and drivers in making informed decisions regarding trip departures, optimum routes, and available modes of travel. ATIS provides the drivers both en route and pre-trip information which are advantageous in many ways. Pre-trip information availability enhances the self-belief of the drivers to use freeways and allows commuters to make better-informed transit choices (Campbell et al., 2003). En route information and guidance saves travel time, helps a traveler avoid congestion, can improve traffic network performance, and is more efficient than paper maps or written instructions. In 1999 a survey was conducted among the people who were using the Advanced Regional Traffic Interactive Management and Information System (ARTIMIS) telephone traveler information service in Cincinnati, Ohio. All of them rated the service as beneficial service. More than 99% of people surveyed in that city said that they were benefited by avoiding traffic problems, saving time, reducing frustration, and arriving at destinations on time and 81% said that they had recommended the service to someone else. In this paper we have discussed the methodology to develop an Advanced Traveler Information System for the Chandigarh city. 2.0 LITERATURE REVIEW Advanced Traveler Information System is being developed and used all over the world. Most of the studies are based in the developed countries and some are also based in the developing countries also. Different platforms and approaches have been used by the different researchers. Some of the literature work has been reviewed here: Peng (1997) presented a method for designing a geographic information systems (GIS)-based automatic transit traveler information system (ATTlS). The idea behind the study was to provide the users optimal trip option with least travel time between the traveler's origin and destination, including walking, waiting, transfer, and in-vehicle time based on their origins, destinations, and bus schedules and/or real-time information of bus locations. To achieve the purpose of providing the optimum route the methodology which was adopted is to consider only those bus stop points which are active (have service) at the time of travel as all the bus stop points don t have the service all 24*7 and considering only active bus stop point results in optimum route. Wu et al. (2003) gave an ATIS based on the Web service and wireless communication technologies and in order to make the data more reliable and useful for the commuters, the methods of lost data reconstruction and travel time prediction were also proposed and examined in the study. An interpolation method was used for the lost data construction based on the periodical behavior of the traffic. Travel time prediction is also done using historical data based on the same observation that the traffic possesses deterministic behavior. The delivery medium which are used in the system are mobile phones and personal computers. Kumar et al. (2005) developed a GIS based advanced traveller information system for the Hyderabad city, India under ArcView GIS environment. The Avenue programming language was used in the source program for the system development. For the process of Path optimization ArcView Network Analyst (AVNA) is used. GIS-enabled modules for the shortest path, closest facility, and city bus routes were included in the system. The developed system provides information about basic facilities in Hyderabad City, such as road networks, hospitals, government and private offices, stadiums, bus and railway stations, and places of tourist interests. The shortest path facility developed in the system gives the user full freedom to choose the origin and destination either by themselves or by given list in the system. 90

97 Framework For Development Of Advanced Traveler Information System: A Case Study For Chandigarh City Hasnat et al. (2006) developed a similar system using web and wireless communication technologies. The system works in two different modules 1. Web Based Service, which provides the service to the user both in text and map format. 2. SMS Based Service, which receives the queries from the users in the pre-defined format and then provide the user information. Like most of the systems it uses Dijkstra algorithm to compute shortest path. In the computation of the total travel time each road/ edge is given a weight based on some constraints as traffic jam etc. and on the basis of this the travel time is calculated. Data Validation System is also included the system to produce the data if any data source stops working to produce the information based on the historical data collected. Singh and kumar (2010) presented an overview of a web based ATIS for developing countries keeping in mind that local traffic, roadway, signalization, demographic, topological, and social conditions in developing countries are quite different from those in developed countries. For the development of system a Web GIS-based architecture was adopted and customized and termed as a Specific Design of Logical Architecture (SDLA). SDLA is the specific adoption of generalized three tier logical architecture which is based on selected SW, database design, and relations between SW components comprising their in-between interaction in terms of data flow and information generation. Three tier architecture used in the system includes: 1. Presentation tier which works as frontend of the system to be used by the users to make the quarries and get the results. 2. Application tier is concerned with the processing of the data according to the user s need and sending this processed information to the presentation tier to give user requested information. 3. Data tier to store the data about the different features. The proposed system uses highway police and traffic regularity authorities as main data acquisition medium whereas desktop computers and information kiosks as the medium for distribution of information. The work of processing of information is done mainly with the help of GIS. Pal and Singh (2011) gave a systematic overview of a GIS system that can be used for structuring, storing and dissemination transit information for transit networks for Metropolitan Cities in India which was capable of handling real-time information. Three-tier client-server SW architecture was adopted as a logical architecture for developing the ATIS. These three tiers which were used are: 1. Presentation tier which works as user interface 2. Application Tier as data processing and information generation rules, and 3. Data tier for the handling of data (storage and management). Whenever a query is made by the user for geospatial analysis web interface passes on request to web server and web server then passes on the request to a Geo server through server connector. The Geo Server processes client requests handed to it by the Web Server, it accesses the spatial data, performs geospatial analysis and renders web-ready map as vector or raster image. The Data Tier is mainly concerned with storage and management of spatial information. Zhang et al. (2011).in their study developed and tested a generic multimodal transport network model for ATIS applications. First, a multimodal transport networks was modelled from an abstract point of view and networks were categorized into private and public modes then a generic method was used to 91

98 Framework For Development Of Advanced Traveler Information System: A Case Study For Chandigarh City construct a multimodal transport network representation by using transfer links which was inspired by the super-network technique. For the computation of shortest path Dijkstra algorithm is deployed. To check the functionality of the developed model and algorithm was tested based on a case study in the Eindhoven region. The biggest problem is that in the time taken for the route determination by integration of different different modes. Mouskos and Greenfeld (1999) developed a GIS-based MATIS which provided travelers with access to information concerning route planning by different modes i.e. private automobile, mass transit, and ride sharing. The System was developed under the ARC/INFO GIS environment and census and graphic data are acquired from the topologically integrated geographic encoding and referencing (TIGER) files from Union County, NJ. 3.0 STUDY AREA For the development of the ATIS we have chosen Chandigarh as our study area. Chandigarh is first planned city of India covering an area of approximately 44.5 square meter or 114 km² and approximate population of 1 million. Chandigarh has well maintained roads and parking spaces all over the city ease local transport. Chandigarh is considered to have one of the best managed traffic and transportation facilities in India but the scenario is changing due to the increasing number of vehicles in the city, this increased population of the vehicles is causing congestion and pollution on the roads. According to the Centre for Science and Environment (CSE) survey (2013): 1. Chandigarh has vehicles per 1000 km of road length whereas Delhi has vehicles per 1000 km of road length. 2. Chandigarh has 227 cars per 1000 people, whereas Delhi has 117 cars per 1000 (2011). So we can see Chandigarh has almost double vehicle density with respect to population and road length as compare to National Capital Delhi. So the need of a welldeveloped ATIS is quite evident in the Chandigarh which will help the users to avoid congestion and spending less time on the roads by providing them both en route and pre-trip informations. 4.0 ATIS DEVELOPMENT 4.1. Methodology Geographic Information System (GIS) platform is used to develop the ATIS. Using GIS environment to develop the ATIS offers many advantages such as it allows large data to be effectively processed, stored, analyzed, logically associated, and graphical displayed. ArcGIS 10.0 software developed by Environmental Systems Research Institute (ESRI) is used in the development of the system. ArcGIS is a very powerful software which provides geographic and spatial analysis and can be used in different ways. In the study the software will be used for the preparation of different layers of the facilities of the Chandīgarh city and their database. ArcGIS 10.0 also provides the facility to add customized features through the programming in Visual Basic for Applications (VBA). So the features for giving the information about the different tourist places of the city and bus routes between a selected source and destination will be added. So the proposed system will be able to provide the following informations Shortest Routes among different places Closest Facilities Service Area of the facilities Information about tourist destinations of the city Bus Routes between a source and destination 4.2. Source Program: Visual Basic for Applications (VBA) will be used to write programs to add the 92

99 Framework For Development Of Advanced Traveler Information System: A Case Study For Chandigarh City customized features in the software, VBA is the implementation of the Microsoft s programming language Visual Basic 6. VBA is widely used to develop user-defined functions, automation and other low level functionalities. Microsoft Access will be used as the database for the customized programs Work Plan: The flow chart of the work plan of the proposed system is given in the following figure 1: 4.4. Input Data: The following data will be taken as Input for the development of the system: Map of the city having a Representative Fraction of 1: Figure 1: Flow Chart of the Work Plan Time Table of City bus Service. Speed limits of the road. Names of the roads. Information of the other facilities. Any type of the constraint on the roads regarding direction and time. 93

100 Framework For Development Of Advanced Traveler Information System: A Case Study For Chandigarh City 4.5. Themes and Layers: By the digitization of the basic map of the Chandigarh city different layers of the different facilities such as Roads, Hospitals, Hotels, ATMs, Petrol Pump etc. have been added in the system which provide the user information regarding these facilities. The Layers which have been added in the System are: Roads: approximately 5000 major and minor roads have been added in the system with the information such as length of the roads, speed limit and name of the road (if any). Transport Facilities: In the transportation facilities approximately 100 bus stops have been included in the system with their names. Railway station and Airport have also included. Educational Institutes: 170 government and private school and colleges with their names have been added in the systems. Hospitals: 150 government and private hospital with their names have been added in the system. Offices: 80 government and private offices with their names have been added in the systems. Tourist Places: Main tourist attractions of the chandigarh with their name, photos and basic information are included in the system. Hotels: 180 hotels with their names have been added in the systems. ATM: 300 ATMs with the name of the bank from which they belong names have been included in the systems. Petrol Pumps: 50 Petrol Pumps with their names have been added in the systems. Sectors: A separate layers to show the names of the sectors is included in the system so that the unknown user can easily findout the sectors in the city. It has been tried to include as much facilities as possible to include in the system to make it more user friendly. The digitized map of Chandigarh city is given below in Figure 2: Figure 2: Digitized Map of Chandigarh City 5.0 OUTPUT OF THE SYSTEM The proposed system will be able to provide the following functionalities to the users: 1. Shortest routes: The proposed System gives the user freedom to choose the point between which he wants to know the shortest route. User can add more than two points also giving them numbers and the system will give the shortest routes between these points according to the numbers from point 1 to 2 to 3 etc. User can also shuffle these points to change their number. The system provides the shortest routes based on two parameters i.e. distance and time. The direction window of the system provides the direction instructions to reach the destination. Figure 3 shows the shortest routes between three selected places: 94

101 Framework For Development Of Advanced Traveler Information System: A Case Study For Chandigarh City based on the users need. The shaded area in the Figure 5 shows the service area of the selected facility: Figure 3: Shortest Route between three 2. Closest facility: There are many facilities included in the system such as hospitals, offices, hotels, educational institute etc. So if the user want to know any closest facility then he can choose the desired facility from the list and mark his position on the map, based on these two parameters the system will provide the closest facilities available near the location of the user. The closest facility is given based on the distance or time parameter set in the system. These parameters can be increased or decreased based on the users need. Below Figure 4 shows the closest facility function of the system: Figure 5: Service Area 4. Information about tourist destinations of the city: This is the customized option which is added in the proposed system. In this option user will be able to choose desired tourist place from a given list and then click give details button, clicking the button will show the picture and basic information about the place. Figure 6 shows the information form of the tourist places in the city: Figure 4: Closest Facility 3. Service area of the facilities: In the proposed system with the help of the service area option the user can also find out the service area of the facilities. The desired service area can be modified with respect to time and distance parameters Figure 6: Information Form of the Tourist Places 5. City bus routes: This is another customized feature added in the system. 95