Analysis of Saturation Flow at Signalized Intersections in Urban Area

Transcription

1 Abstract Number: 239 Analysis of Saturation Flow at Signalized Intersections in Urban Area Subhash Chand* Neelam J Gupta** Nimesh Kumar*** * Principal Scientist, Traffic Engineering and Safety Division, CSIR-CRRI, New Delhi, , sbk.crri@nic.in ** Senior Scientist, Traffic Engineering and Safety Division, CSIR-CRRI, New Delhi, neelamjn19@gmail.com *** M. Tech. Student, Civil Engineering Department, NIT Hamirpur, nimesh392@gmail.com Abstract: At-grade intersections are one of the most critical elements that influence the performance of urban road network. For safe and efficient movement of large volumes of traffic on city road network, majority of the intersections are usually signalized. The design, the capacity and operation of a signalized intersection critically depend on and passenger car unit (PCU) and saturation flow. Operation and performance of signalized intersections is influenced by the roadway parameters, traffic condition, operating parameters and environmental conditions along with user s behavioural characteristics, which significantly differ among locations. These factors have been traditionally measured, in most of the western countries, based on the research carried on test tracks and on public roads where traffic is typically car-dominated with vehicles moving in clearly defined lanes. The intersections on urban roads in India cater to heterogeneous motorised traffic along with slow-moving traffic including pedestrians. It is therefore necessary to consider passenger car unit (PCU) and saturation flow for mixed traffic conditions to evaluate the overall operation of signalized intersections. This paper presents the results of the study on analyses of saturation flow rate conducted at signalized intersections with mixed traffic condition in Delhi, India as part of research study for development of Indian Highway Capacity Manual (Indo-HCM) sponsored by CSIR. Keywords: Signalised Intersection, Saturation Flow, Mixed Traffic, Passenger Car Unit (PCU) 1

2 1. Introduction India is a developing country and its cities are undergoing rapid urbanization and modernization as a result there is rapid growth in the road traffic. Traffic movement in India is very complex due to the heterogeneous traffic stream sharing the same carriageway. Also despite having lane markings, most of the times lane discipline is not followed particularly at intersections. Highway Capacity Manual and other works assume homogeneous and lane based traffic for analysis, which exists in developed countries. There is notable lateral movement at intersections and vehicles tend to use lateral gaps to reach the head of the queue and overtake even during saturated part of green phase. Due to these fundamental differences, the standard western relationships for predicting the values of saturation flows and PCU factors are not appropriate for developing countries like India. For correct signal design these parameters should be estimated based on the local prevailing traffic conditions and hence requires a different approach of analysis. There are no proper guidelines available to estimate saturation flow for non-lane based heterogeneous traffic conditions. Effect of lack of lane discipline on capacity analysis needs to be considered. The most significant parameter that influences the design of signalised intersection and its signal plan is the saturation flow. Saturation flow is a key factor determining the capacity and level of Service (LOS) of a signalized intersection. If the saturation flow rate can be computed to the reasonable accuracy, the capacity of the signalized intersection can be evaluated. In the present study, actual classified vehicular traffic flow during saturated green intervals of the green phases has been measured in the field at different approaches of four intersections to calculate the saturation flow. PCU values for different vehicles have been estimated based on area and clearance time ratio of different vehicles as compared with that of the standard car as obtained from field data. Saturation flow obtained in terms of number of different vehicles have been converted into PCU by applying IRC value obtained from the field and that using the PCU factors as per IRC-SP-41. Models have also been developed for estimation of saturation flow for different approach widths and different percentage of two wheelers and cars at signalized intersections for non-lane based mixed traffic conditions of Delhi and Noida. The paper further compares the results of saturation flow as obtained from the derived model and actual field saturation flow obtained using field estimated PCU values with that obtained using the U.K model and PCU factors as per IRC-SP-41. It is found from the analysis that the derived model gives better results. 2. Study Area Fourteen approaches of four near ideal 4-arm pre- timed signalized intersections were selected for the present study having different approach widths ranging from 6.4m to 14.5m. Intersections selected for this study are right angle intersections and have level gradient on all approaches and least interference to entry or exit traffic due to pedestrians, bus stops, parked vehicles, etc. All the approaches of the intersections reach saturated stage for whole or majority of the green interval during almost each phase during peak hour as traffic flow is very heavy. The traffic does not follow lane discipline and consists of more than 12 different types of vehicles varying in speed and sizes. Four selected signalized intersections are: 1- Stadium Chowk (Noida), 2- NTPC Chowk (Noida), 3- DTC Depot Chowk, Dwarka Sector 2 (New Delhi) and 4-Deepali Chowk, Pitampura (New Delhi). 3. Study Methodology Literature review reveals that little work has been done towards the effect of heterogeneity of traffic on saturation flow and performance and capacity analysis of signalized intersections. Highway Capacity Manual (HCM) provides basis for the capacity analysis based on estimation of saturation flow based on headway measurements at stop line during saturated flow condition for ideal base conditions of uniform traffic and lane deriving and applying adjustment factors to account for different influencing parameters. This approach is being widely used in most of the developed countries as it represents their traffic conditions. In the present study attempt has been made to measure saturation flow in the field by actually measuring the flow at the stop line during saturated green phase and to study the impact of various influencing parameters such as road widths, traffic composition etc. based on actual field studies/experiments of the typical Indian traffic conditions. The systematic flow chart of methodology of this research work is depicted in Figure 1. 2

3 Identification of Signalized Intersection for Study Collection of Data on Roadway/ Intersection Geometric Parameters Collection of Traffic Flow Data by Videography Survey Fifteen Minutes Classified Traffic Data Extraction in Laboratory Collection of Data on Operational Parameters Conversion of fifteen minutes data in to hourly data Peak hour identification Extraction of traffic data for saturated green intervals Vehicle Clearance Time Measurement Vehicles Area Measurement Estimation of PCU Calculation of Saturation Flow (i) Using IRC SP-41 PCU value (ii) Using Estimated PCU Value (iii) IRC SP-41Formula (S=525W) Development of Saturation Flow Models Figure 1: Methodology of the Study Field surveys were done in order to collect the following parameters: Roadway/Approach conditions and operational parameters Traffic conditions As part of roadway conditions data measurement of all approach widths at stop line, length and widths of taper, width of median, width of left slip roads, sidewalks, size of channelizing islands etc. were taken manually on site by measuring tape and measuring wheel. The number of lanes for turning traffic in each direction viz. straight/ through (TH) and right (RT) were also noted. The signal timing for each 3

4 phase of each approach was noted manually for all the intersections. Roadway condition and operational data for different approaches for all the selected intersections are given in Table 1. Intersection Stadium Chowk ( Noida ) NTPC Chowk ( Noida ) DTC Depot Chowk, Dwarka sec 2 ( Delhi ) Deepali Chowk, Pitampura ( Delhi ) Table 1: Geometric and Operational details of the Intersections Traffic Approach From Width ( m ) Cycle time ( s ) Green time Amber Time Red Time Spice mall ( NB ) Noida Mor (SB ) DND ( EB ) Chora Mor ( WB ) GIP ( EB ) Ghaziabad ( WB ) Dabri Ext. (WB) Dwarka sec 10 (EB) Madhu Vihar (SB) Palam Vihar (NB) 10.0 ` Madhuban Chowk (WB) Peera Garhi (EB) Rohini (SB) Rani Bagh (NB) EB = East Bound, WB = West Bound, NB = North Bound, SB = South Bound Traffic condition data deals with the field traffic flow patterns (traffic volume) of different turning movements, traffic composition, speed/clearance time of different vehicles at each phase of the signal at different approaches of the signalized intersections. In this study, traffic turning movement data of the subject approaches of the intersections was recorded by using a portable digital video camera mounted on the 6m (20 ft) high stand at the opposite island or median or at a vantage point at the corner of the intersection to cover all, three/two or one of the approaches of the intersection so that it clearly capture view of approach road from exit line (line joining ends of Channelizing islands) of both the through (TH) and right (RT) movements up to about 10 m inside the stop line on the subject approach as shown in Figure 2. Continuous pictures of the traffic flow were recorded with the video camera for peak morning period of 2 to 3 hours between 9:00 am to 12:00 noon on normal week days. Simultaneously data on signal timing i.e. cycle length, number of phases and phase length was collected manually. Figure 2: View of Stadium Intersection from Vantage Point and Camera mounted Portable 6m High Camera Stand The recorded films were replayed in the laboratory on a large screen in order to retrieve the required data information for the study. In the first phase 15 minute classified traffic turning movement data were retrieved for entire duration of survey for all the approaches in order to determine peak hour, peak hour 4

5 traffic volumes, peak factor, traffic composition, percentage of turning traffic etc. for each turning movement, approach and intersection. Saturated green period for the green time of the subject movement was taken from 5 seconds after the onset of the green phase till the end of dissipation of queue length. In order to retrieve the saturated green intervals for individual green phases during peak hour video records were replayed for individual green phases before actually retrieving the classified turning movement data for individual saturated green intervals of signal phases of the subject approaches. During each saturated green interval of peak hour clearance time of different vehicles were also retrieved on random sample bases in order to estimate PCU values during saturated flow. Clearance time was taken as time of vehicle occupying the intersection common space/area from end of kerb line at entry to end of kerb line at exit (entry of front bumper at entry line to exit of rear bumper at exit line). PCU values were calculated using equation developed by Chandra and Kumar [9] and using these values saturation flow was estimated in PCU per hour for each approach. Finally, saturation flow model is developed for non-lane based mixed Indian traffic conditions. 4. Data Analysis Data analysis of various traffic characteristics such as traffic volume, traffic composition, peak hour traffic volume, peak hour factor and traffic composition etc. was done for each turning movement, each approach and intersection as whole for each intersection. Classified traffic flow data for saturated green intervals of all the phases of through (TH) and right turning (RT) movements were added to calculate the average saturation flow (number of vehicles per hour green) as there were no segregated lane for through (TH) and right turning (RT) movements of the different approaches. PCU factors for different classes of vehicles were estimated for saturated flow condition using equation developed by Chandra and Kumar [9] and using these values saturation flow were estimated in PCU per hour for each approach as shown in Table 2. PCU i = Passenger Car Unit of vehicle type i A i = Area of i th vehicle A c = Area of passenger car V i = Average clearing speed of vehicle type i in m/s V c = Average clearing speed of car in m/s t c = Average clearing time of car in sec t i = Average clearing time of vehicle type i in sec Table 2: Estimated PCU values of Different Vehicles at Different Approaches Approach road Approach Cars / Jeeps/Taxi/Van Three Two Non Motorized Traffic Width Wheelers Wheelers (NMT) Big Car Small Car (Auto- Rickshaw) (Scooters / Motor Bicycles Cycle Rickshaws Cycles) Spice mall ( NB) Noida Mor (SB DND (EB) Chora Mor (WB) GIP (EB) Ghaziabad (WB) Average Saturation flow was calculated for each approach by using formula given below: S= Saturation Flow in vehicle/h OR (PCU/h) S = x 3600 Classified average saturation flow for combined through (TH) and right turning (RT) movements of different approaches of the selected intersections were converted into Passenger car unit (PCU) by multiplying the respective PCU factors estimated in this study with the number of vehicles of the category 5

6 in order to derive average saturation flow in PCU per hour green. Same procedure was used to calculate saturation flow in PCU per hour green by using PCU values from IRC SP-41. A generalized formula is given in IRC SP-41 for direct calculation of saturation flow on the basis of road width i.e. S=525*W in absences of realistic saturation flow value. Using this formula the saturation flow for different approaches was also estimated for comparison purpose only. 4.1 Comparison of Saturation Flow Obtained Using Estimated PCU, IRC SP-41 PCU and IRC SP-41 Empirical Formula Classified saturation flow data of all the peak hour cycles were used to get average saturation flow for each approach. The measured value of anverage Saturation Flow of different approaches of study intersections expressed in terms of PCU per hour using estimated PCU values, IRC SP- 41 PCU values [25] and estimated as per equation S=525W as given by IRC SP-41as per UK method has been presented in Table 3. The average saturation flow of different approaches is found to vary at different approaches. It is also found that average saturation flow obtained through field studies is higher in bothe cases when expressed using field estimated PCU values and IRC SP-41 PCU values than the saturation flow obtained by generalized formula S=525*W of IRC SP-41. It is also found that saturation flow measured in the field using IRC SP-41 PCU values is not consistent with the widths of approaches. The value of saturation flow for approach width of 9.7 m is lower than that for 9.4 m approach width. Simillarly the saturation flow for approach width 10.3 m and 11.7m are lower than that of approach widths of 10.0 m and 11.0 m respectively and is contradicting. While the saturation flow obtained in the field using estiamated PCU values from (field data) are found to be consistent with approach widths and reflects affect of small variation in widths logically. Table 3: Comparison of Measured Saturation Flow (pcu/h) of Different Approaches Calculated using Different PCU factors/ Methods Saturation flow (PCU/h) Intersection Stadium Chowk (Noida) NTPC Chowk (Noida) DTC Depot Chowk, Dwarka sector 2 ( Delhi ) Approach Name Width ( m ) Field Estimated PCU As per IRC SP-41 PCU As per IRC SP-41 formula S=525*W Spice mall ( NB ) Noida Mor (SB ) DND ( EB ) Chora Mor ( WB ) GIP ( EB ) Ghaziabad ( WB ) Dabri Ext. (WB) Dwarka sec 10 (EB) Madhu Vihar (SB) Palam Vihar (NB) It is worth to further mention here that the saturation flow per meter width obtained from field estimation of PCU values is on the quite high (760 PCU/m) as compared to 525 PCUs per meter width given in IRC SP- 41 for all the approaches of the selected intersections as shown in Figure 3. This may be attributed to (a) appriciable higher percentage of big car and (b) higher percentage of two wheeler filling the gaps between the larger vehicles during saturated flow. The result may be compared and further analysed by taking big car as reference vehicle in place of small car for estimation of PCU factors for different vehicles for saturation flow estimation. The value of saturation flow per meter width obtained from field estimaed PCU values is observed to be quite oconsistent with the average value. 6

7 Saturation Flow (pcu/h) Figure 3: Variation of Saturation Flow (per metre width) at Different Approaches as Obtained by Different Methods 5. Development of regression models Regression model was developed between average saturation flow and approach widths (Figure 4). Regression model were also developed between percentage of two wheelers and car and saturation flow to understand the variation in saturation flow during different green intervals of same approaches (Figure 5). This study also presents the results of validation of the saturation flow model developed and compares the results with the values obtained by using formula given in IRC SP 41 and also by using static values of PCU given in IRC SP 41 (Table 4). (i) Correlation between average saturation flow and width of approaches: S = Saturation Flow in PCU/hour w= Width of approach in meters. S= - 63w w 7043 Approach Width (m) Figure 4: Correlation Between Saturation Fow (pcu/h) and Approach Width In order to validate the developed correlation model between saturation flow and approach widths using data of 10 approaches of thee intersections, four approaches of Deepali Chowk intersection having different approach widths were considered for validation. Field values of saturation flow were compared with the estimated value of the saturation flow using the model for four approaches of Deepali Chowk. The results of model validation for different approach widths of Deepali Chowk are represented in Table 4. 7

8 Saturation Flow (pcu/m/h) Table 4: Results of Model Validation Traffic Approach From Approach Saturation Flow (pcu/h) Width IRC- IRC SP-41 Estimated Developed PCU (S=525*W) PCU Model Rani Bagh (NB) Rohini (SB) Peera Garhi (EB) Madhuban chowk (WB) Comparison of the measured value of saturation flow using estimated PCU values with that using IRC SP-41 PCU values shows that there is average difference of 5% to 30% at different approaches. The developed model gives higher value (28 to 31%) and consistent value of saturation flow as compared to IRC SP-41 generalized equation S=525W. (ii) Linear Correlation between saturation flow and percentage of two wheeler developed for all approaches: S= 9.61*Tp S = Saturation flow (PCU/h) Tp = Average percent of two wheeler (iii) Linear Correlation between saturation flow and percentage of car developed for all approaches: S= -5.39*Cp S = Saturation flow (PCU/h) Cp = Average percent of Cars Percentage of Two Wheeler Percentage of Cars Figure 5: Correlation Between Saturation Flow and Percentage of Two-Wheelers and Small Cars It is found that with increasing proportion of two wheeler, saturation flow per meter width also tends to increase due to heterogeneity and filling of gaps by two wheelers, while with increase in proportion of cars the saturation flow tend to decrease due to more homogeneity. 8

9 6. Conclusions and Recommendations The study clearly emphasize the need for estimation of PCU values based on actual field studies at the signalised intersections for their analysis and performance as these are found to vary considerably as compared to IRC PCU values. Estmated PCU values are observed to give higher but consistant value of saturation flow for different approach widths as compared to IRC-PCU values. Estimated PCU values give consistent value of saturation flow per metre width of approach for all the approaches. But Estimated values of PCU fail to explain the variation of saturated flow during different saturated green phases of same approach which may be utributed its senstivity to composition and the varying composition of traffic during different green phases of signal.it affirms that PCU values at signalised intersections are highly dynamic and further emphasises the need of estmation of PCU values based on different comprehensive approach. The developed corelation model between average saturation flow and approach widths gives higher (28 to 31%) and consistent value of saturation flow for different approach widths as compared to IRC SP-41 generalized equation S=525W. It is found that with increasing proportion of two wheeler, saturation flow per meter width also tends to increase due to heterogeneity and filling of gaps by two wheelers, while with increase in proportion of cars the saturation flow tend to decrease due to more homogeneity. The regression model developed for saturation flow is based on traffic conditions at the selected signalized intersections in NOIDA and Delhi. This model have been developed for combined movement of through and right turning traffic and may further be developed for through (TH) and right (RT) turning traffic separately. Further studies based on comprehensive data is needed to establish reliable model for general application, especially for varying geometric, traffic and environmental conditions. This study can be used as a baseline for further research work on analysis of traffic flow at signalized intersection in Delhi as well as in other cities of India to further develop and update the derived relationship between saturation flow and approach width, percentage of different classes of vehicles etc. Saturation flow based on entire width is expected to provide better understanding of relationship, particularly in India where traffic highely hetrogeneous and there is no lane discipline. More intersections located in different parts of the city with different approach widths and varying traffic characteristics and pedestrian movement, be studied for further refinement and updation of present equation / models. Acknowledgements: The authors are grateful to Director CSIR CRRI to accord his permission to publish this paper. References: 1. Abu-Rahmeh, F.W (1982), Saturation flow and lost time at traffic signals Ph.D. Thesis Department of Civil and Structural Engineering, University of Sheffield. 2. Al Shu-bo and Y ANG Xiao-kuan (2009), Capacity of Dual-Right-Turn Lanes at Signalized Intersections under Mixed Traffic Conditions. 3. Al-Ghamdi, Ali S, (1999). Entering Headway for Through Movements at Urban Signalized Intersections. In Transportation Research Record: Journal of the Transportation Research Board, No. 1678, TRB, National Research Council, Washington, DC, pp Ashworth R. (1976) A Video Tape recording system for data collection and analysis, Traffic Engineering and Control, vol Australian Road Research Board, (1968), Australian Road Capacity Guide Provisional Introduction and Signalized intersection, ARRB Bulletin No Branston, D., and Van Zuylen, H. (1978). The estimation of saturation flow, effective green time and passenger car equivalents at traffic signals by multiple linear regression. Transportation Research, Vol. 12(1), pp C. S. Anusha; Ashish Verma, Aff.M.ASCE; and G. Kavitha (2013) Effects of Two-Wheelers on Saturation Flow at Signalized Intersections in Developing Countries. 8. Cartagena,R.I. and Tarko,A.P.(2005) Calibration of Capacity Parameters for Signalized Intersections in Indiana, Journal of Transportation Engineering, Vol. 131, No. 12, Dec Chandra, S. and Kumar, U. (2003). Effect of Lane Width on Capacity Under Mixed Traffic Conditions in India, ASCE Journal of Transportation, 129(2), pp Chandra, S. and Sikdar, P.K. (2000), Factors Affecting PCU in Mixed Traffic Situations in Urban Roads. 11. Chang-qiao SHAO, Jian RONG, Xiao-ming (2011), Study on the Saturation flow rate and its influence factors at signalized intersection in china. 9

10 12. Chang Chien (1978), Saturation Flow at Signal Controlled intersection in Bangkok MSc Thesis, Asian Institute of Technology, Bangkok, Thailand. 13. D. L. Gerlough and F. A. Wagner (1967). NCHRP Report 32: Improved Criteria for Traffic Signals at Individual Intersections. Highway Research Board, National Research Council, Washington, D. C., 1976, pp DingXin Cheng; Zong Z. Tian; and Carroll J.Messer,(2005) Development of an Improved Cycle Length Model over the Highway capacity Manual 2000 Quick Estimation Method, Journal of Transportation Engineering, Vol. 131, No. 12,December 1, G. Dhinakaran and R. Prasanna Kumar (2012), Estimation of delay at signalized intersections for mixed traffic conditions of a developing country, International Journal of Civil Engineering, Vol. 11, No Greenshields, B.D., Shapiro, D., and Ericksen, E.L. (1947). Traffic Performance at Urban Intersections, Technical Report No.1. Bureau of Highway Traffic, Yale University. 17. Helm B. (1961), Saturation Flow at Light Controlled Intersections, Traffic Engineering and Control. 18. HCM (Highway Capacity Manual) 4 th Edition (2000). Transportation Research Board, Washington D.C. 19. Highway Capacity Manual, 2000, Transportation Research Board, Washington D.C: Chapter Holt, Daniel Lester (2004), The Effects of Bus Stops on the Saturation Flow Rate of Signalized Intersections. Unpublished Master s Thesis, North Carolina State University. 21. Hossain, M.(2001), "Estimation of saturation flow at signalised intersections of developing cities: a micro-simulation modelling approach", Transportation Research Part A, Ibrahim et. al. (2002) had carried out a study to determine the ideal saturation flow at signalized intersections under Malaysian road conditions. They adopted the method of measuring saturation flow published by the (then) Road Research Laboratory. 23. Ingrid B. Potts et, al (2007). Relationship of Lane Width to Saturation Flow Rate on Urban and Suburban Signalized Intersection Approaches, Transportation Research Record 2027, TRB, National Research Council, Washington, D.C., pp IRC:64 (1990), Guidelines on Capacity of Roads in Rural Area, (First Revision), Indian Code of Practice, Indian Roads Congress. 25. IRC-SP:41 (1994), Guidelines for Design of At-Grade Intersection in Rural and Urban Areas, Indian Code of Practice, Indian Roads Congress. 26. Justo, C. E. G. and Tuladhar, S. B. S. (1984), Passenger Car Unit Values for Urban Roads, Journal of the Indian Road Congress, Volume 45-1, pp Khan, A.M. (1995), Application of Geographic Information Systems (GIS) to Urban Transportation Planning and Management. United Nation s Seminar on Urban Geographic Information Systems, City Sustainability & Environment, Cairo Egypt, December 10-14, Kimber, R. M., McDonald, M. and Hounsell, N. (1985), Passenger Car Units in Saturation Flows: Concept, Definition, Derivation, Transportation Research B, Volume 198, No. 1, pp M. Hadiuzzaman (2008), Development of Saturation Flow and Delay Models for Signalized Intersection in Dhaka city, M. Sc. Engg (Civil & Transportation) Thesis, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. 30. Maini, P. and Khan, S. I. (1999), " Modeling Heterogeneous Traffic Flow ", Transportation Research Record 1678, TRB, National Research Council, Washington, D.C., pp Miller, A.J (1968), The capacity of signalized intersection in Australia, Australian Road Research Board. Bulletin No Road Research Laboratory (1963), A method of measuring Saturation Flow at Traffic Signals, Road Note No.34.HMSO, London. 33. Scraggs, D.A. (1964), Determination of passenger car equivalent of Goods Vehicle in Single Lane Flow at Traffic Signals, RRL Report LN/572/DAS, Road Research Laboratory. 34. Sutaria, T. C. and Haynes, J. J. (1977), Level of Service at Signalized Intersections, Transportation Research Record 644, TRB, National Research Council, Washington, D.C., pp Transportation Research Board. (1997). Special Report 209: Highway Capacity Manual, National Research Council, Washington, D.C. 36. Webster, F.V and Cobbe (1966), Traffic Signals, Road Research Technical Paper No.56, HMSO, London. 10