Improvement in Transit Service using GIS Case study of Bhavnagar State Transport Depot Mukti Advani Research Scholar, TRIPP/IIT Delhi B.Srirama Research Scientist, TRIPP/IIT Delhi S.K.Pathan Head, LPPD, SAC-ISRO, Ahmedabad Abstract: Geographical Information System (GIS) is a tool which has been employed for integration of spatial and non-spatial data. GIS is a specific integrated system of hardware, software and procedure designed to support capture, management, manipulation, analysis and display of spatially referenced data for solving complex planning and management problems. GIS can be applied to any service that is dependent on network like water supply, power supply, sewage, etc. So it could be of great help for Transportation Engineering and planning also. When so many parameters are to be connected with Transportation network like travel time, speed, road resistance, turning movements, etc. For such a big network GIS proves itself as an efficient tool for solving such a network problems quickly and with a great precision. A case study of Bhavnagar district area was conducted for determining the optimal routes from one origin to many destinations kind of problem, with an objective of minimizing travel distance and travel time of users. Constrains taken into consideration were impedance for intersections, type of road and speed. GIS emerged as better tool for getting solution of such complex problems very accurately and rapidly. Introduction A country s transportation system represents development stage of country. But at the same time highly developed countries are facing higher problems of transportation management and spending lots money and effort for solving those problems. Growing traffic congestion, the need to preserve the environment, and the problems of road safety are the main reasons for many cities worldwide to consider new initiatives in public transit systems. The complexities of building and operating the transport system efficiency and safely have out stripped the ability of past experience and professional judgment alone to provide solutions. If a country is to satisfy the transport infrastructure requirement in consonance to its developmental pace, decisions must be based on a more reliable, updated, relevant, easily accessible and affordable information. Better information does not guarantee better decision making capability but its absence surely precludes it. The application of GIS to a diverse range of problems in Transportation engineering is now well established. It is a powerful tool for the analysis of both spatial and non-spatial data and for solving important problems of networking. Proceedings ESRI National Conference 2005 held at NOIDA, India. 1
Shortest path analysis is an essential precursor to many GIS operations. Zhan (1996) has worked on this and explored the use of fast shortest path algorithm on extensive road networks. Pathan (1994) has evaluated the possibilities of optimization, in which the optimum routes, travel time, travel distance and cost for defined paths and for the optimum paths was determined for few transport services. Guruswamy (1989) has also evaluated the GIS techniques for route optimization. In this study Chennai city has been taken for finding out the optimal route for emergency services and that for time taken for service is on top most priority. Steven (2003) has developed a genetic algorithm to optimize a bus transit system serving an irregularly shaped area with a grid street network. The total cost function is minimized subject to realistic demand distribution and street pattern. Crowson et al. (1997) have developed a geographic information system that includes the street maps for the three-country service region, the route system, and the bus stop locations. These maps are used together with US census block and block group information to perform communication, analysis, planning and service assurance. Previous studies have highlighted the need for tools to assess the impact of interventions on the bus network and the accessibility of the system. Belinda (2003) concludes that using GIS, the analysis of transport disadvantage and accessibility is possible. Their paper provides an analysis of the city bus network in Northern Ireland and assesses the spatial impact of hypothetical network changes on populations residing within the city bus network area. Within the GIS environment, data sets are displayed in a range of innovative ways (3-D, grid and other thematic maps) to facilitate data interpretation. Zhong-Ren Peng and Ruihong Huang (2000) present web-based transit information system design that uses Internet Geographic Information Systems (GIS) technologies to integrate Web serving, GIS processing, network analysis and database management. A path finding algorithm for transit network is proposed to handle the special characteristics of transit networks, e.g., time-dependent services, common bus lines on the same street, and nonsymmetric routing with respect to an origin/destination pair. The algorithm takes into account the overall level of services and service schedule on a route to determine the shortest path and transfer points. A framework is created to categorize the development of transit information systems on the basis of content and functionality, from simple static schedule display to more sophisticated real time transit information systems. A unique feature of the reported Webbased transit information system is the Internet-GIS based system with an interactive map interface. This enables the user to interact with information on transit routes, schedules, and trip itinerary planning. Some map rendering, querying, and network analysis functions are also provided. Belinda M. Wu and Julian P. Hine conclude that using GIS, the analysis of transport disadvantage and accessibility is possible. Their paper provides an analysis of the city bus network in Northern Ireland and assesses the spatial impact of hypothetical network changes on populations residing within the city bus network area. Within the GIS environment, data sets are displayed in a range of innovative ways (3-D, grid and other thematic maps) to facilitate data interpretation. Marius Theriault et al. (1999) presents a modeling and simulation procedure to evaluate optimal routes and to compute travel times for each individual trip of an OD survey database. 2
Postal codes provide accurate locations within street blocks for each trip beginning and end point. Using TransCAD GIS software, the procedure finds the best routes through a topological road network. Each road is characterized by a maximal speed related to the functional class of the road, to its location in rural or urban areas, and to the distance from the nearest school. Turn and transfer penalties govern movements at the intersections. Moreover, the procedure calculates the number of persons traveling on every road to estimate traffic congestion. Through study it is concluded that it is possible to combine GIS and transportation modeling to estimate travel time of urban commuters. This could help measuring temporal constraints of households in planning their daily activities. Ali M. (2003) has focused on the two related issues of employment distribution and access to transit services. It is primarily concerned with identifying some of the major employment centers in the county and how they may be understood within the context of a polycentric metropolitan area. Using the 2001 census tract level economic activities and the 2000 census of population and housing, a number of analyses were performed that identified some of the major employment centers and subcenters in the metropolitan areas. A GIS-friendly software is used that allows calculations of various clustering patterns for major employers, total employment in 2001. This software performs an NNH (nearest neighbor hierarchical) spatial clustering routine that group s data points together on the basis of spatial proximity (using a threshold distance and the minimum number of points required for each cluster). Transit accessibility is relatively high for a majority of subcenter clusters; the geography of job-housing balance suggests a high level of spatial mismatch in the region, especially for the low-income and African-American populations. With the exception of a few locations, transit-rich areas are geographic spikes that converge on the downtown area. This creates a number of problems. For example, while employment sub centers are adequately networked by the existing bus routes, the connection between employees and their place of work appears to be inadequate. This is especially true for connections that require a north south movement. In fact, the geography of transit network in the county evidences a preponderance of eastwest connections. This has created a sub optimal condition in many sections of the metropolitan area. Author concludes that the transit network should be conceptualized not only as the backbone of economy, but also as an urban service that enhances the economic vitality of all sectors of the society especially those with the most need for mobility and improvement in their level of access to jobs and other urban amenities. Study area In this study, prime objective was to determine the optimization of transport service using GIS network analysis techniques for the area of State Transport Depot - Bhavnagar District. The detailed objectives were: To prepare digital data of road network map of Bhavnagar district using existing available data on 1:250000 scale. To develop a methodology for route optimization in GIS environment. To determine the optimal routes between various origin destination points. To evaluate the distances and oil consumption in the optimal transport service. 3
The Bhavnagar city and its nearby villages covering an area of about 8154 Sq.Km. have been selected for the study. This area includes 11 different Talukas of the district i.e. Ghogha, Sihore, Valabhipur, Mahuva, Talaja, Botad, Gadhada, Gariyadhar, Palitana and Umrala. This area is bounded by 21º 00 22º 30 latitudes and 71º 00-72º 30 longitudes. Details of the base spatial data acquired for this study area are Survey of India topographic maps. 41 N 46 B 41 O 46 C These sheets were prepared on the basis of survey conducted in the year 1965-68 and it has been employed for preparing base maps for entire study areas. The road network map has been updated using existing maps of GSRTC guide maps and RS (remote sensing) data. For attributes, different data i.e. average speed of buses for different types of roads, cost of fuel used, etc. is collected from the S.T. department of Bhavnagar. Information about existing routes has been collected from Form no. 4 of S.T. records. SOI map RS data City guide map Tabular data Base map Image interpretation Road network map Road resistance Digitization Road network coverage with attributes Turn impedance Network analysis Final optimum path 4
Impedances applied: Any network analysis requires impedances associated with each road in the network. By which we can give priorities to the different roads for analysis. Such impedances are very important in optimal route determination. Two types of impedances are considered in this study: Resistance on different types of roads. Within the study area of Bhavnagar, there are roads of different types i.e. National highway, State highway, Major and Minor roads. Speed for different roads is also different as written in table below: Type of road Speed (km/hr) National highway 60 State highway 45 Major roads 40 Minor roads 30 assigned Turn impedance at the intersections of roads. At the junction point of roads, resistance occurs due to turning or due to interferences of other vehicles. How much delay will occur due to such an intersections is depends on the movement of the vehicle. Resistance differs as per the angle of movement. Movement Impedance (in seconds) U turn 60 Right turn 45 Left turn 15 straight 10 Railway crossing 30 In many cases, one may want to assign an impedance value to a turn or to prevent certain turns. The turn impedance may be any positive numeric value. A negative value in the turn impedance item signifies that the turn is prohibited. Assigning all information to GIS network and after performing network analysis, these gives the optimal route with the information about the direction, time required and length of the each segment of the road as well as the total length and time for the route. Results Length of 49 different routes obtained from GIS has been compared with the length data collected from S.T. Department of Bhavnagar. Out of 49 routes 45 routes are optimized and 4 routes such that the routes are having less length then the routes suggested by the GIS network tool. The reasons behind this are as listed below: 5
Route no. 11 Bhavnagar to Dholera Present route is operated on newly constructed coastal road in this area which is not available in the database. Route no. 19 Bhavnagar to Bhumbhli In this area a new Kachha road has been constructed after 1965-68 so that road was not in the database. Route no. 26 Bhavnagar to Chuda Bus is running on cart-track also in this area to reach up to the settlement of Chuda and all cart tracks are not considered in the database. Route no. 30 Bhavnagar to Nagdhaniba Bus-stop is away from the actual settlement. People uses shuttle rickshaw to reach up to the actual village area. Table shows the comparison of existing routes and calculated routes by network analysis. Item / Matter Existing optimum Saving Distance/year (km) 1274907 1252668 20249 Diesel/year (liter) 182129 178952 3177 Cost/year (rupees) 3460463 3400099 60364 On an average S.T. Department of Bhavnagar can save an amount of Rs. 60365/- per year on fuel consumption itself. Though, this saving is a variable as the diesel cost varies from time to time. Moreover this saving does not include the saving from wear and tear. Hence S.T. can save little more amount of wear and tear, if it plans transport service on optimum routes. Conclusions: The information obtained from the network analysis using GIS shows that, The total travel distance has come down from 1274907 Km ton 1252668 Km per year. Diesel consumption has come down from 182129 liters to 178952 liters per year. On an average Bhavnagar depot can save an amount of Rs. 60365/- per year on fuel consumption itself. The saving is variable as the diesel cost varies from time to time. Moreover this saving does not include the saving from wear and tear. Hence Bhavnagar depot can save more amounts not only on fuel but also on wear and tear. The limitations encountered in the analysis are: Road network coverage is prepared based on 1965-68 data. Latest road network is required for better route optimization. The latest coverage should include all new roads viz. highway, minor and major roads which have come up after 1968. In this study, all NH, SH, Major and Minor roads are considered for the route optimization. However, it has been observed that S.T. buses are running even on cart tracks also. Hence, there is a need to incorporate cart tracks for optimization. 6
References: Papers: Ali M.(2003), Polycentricity and transit service, transportation research paart A, volume 37, issue 10, pp 841-864. Belinda (2003), A PTAL approach to measuring changes in bus service accessibility, transport policy, volume 10, issue 4, pp 307-320 Crowson (19997), A GIS for public transit, ESRI conference, San Diego, CA. Guruswamy V. (2000) Optimal route analysis using IGS, MapIndia2000, at http://www.gisdevelopment.net. Laplame G. (1992) A geographic information system for transportation applications, communications of the ACM 1992; (35): page 80-88. Marius T. (1999), Modelling commuter trip length and duration within GIS: application to an O-D survey, journal of geographic information and decision analysis, vol 3,issue 1, pp 40-56. Papacostas C. (2004), GIS application to the monitoring of bus operations http://www.eng.hawaii.edu/~csp/mygis/busgis.html Pathan (1994), Optimization of Transportation Routes using GIS techniques Sankar R (2003), Optimization of bus stop locations using GIS as a tool for Chennai city - a case study MapIndia conference. Steven C. (2003), Optimization of multiple route feeder bus service - an application of GIS. Journal of Transportation Research Board 2003(3). Zhong R. (2000), Design and development of interactive trip planning for web-based transit information systems, tranporation research part C, volume 8, issue 1-6, pp 409-425 Books: Rashid Remote sensing in Geography, Manak publications Pvt. Ltd. Paul A, Longley, Michel F., Goodchild, Geographical Information System Volume I & II. ARC/INFO GIS Manuals, Environment System Research Institute, Redlands, USA. 7