CAPACITY AND DYNAMIC PASSENGER CAR UNIT ESTIMATION FOR HETEROGENEOUS TRAFFIC STREAM OF URBAN ARTERIALS: A CASE STUDY OF INDIAN METROPOLIS

Transcription

1 CAPACITY AND DYNAMIC PASSENGER CAR UNIT ESTIMATION FOR HETEROGENEOUS TRAFFIC STREAM OF URBAN ARTERIALS: A CASE STUDY OF INDIAN METROPOLIS Satyajit Mondal, Corresponding Author Research Scholar, Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi , U.P. India, Mob No: ; satyajit07iiest@gmail.com Sandip Chakraborty Assistant Professor Department of Civil Engineering, IIEST, Shibpur, Howrah, , India Mob No: ; sandipchakrabortyiiest@gmail.com Sudip Kumar Roy Professor Department of Civil Engineering, IIEST, Shibpur, Howrah, , India Mob No: ; royksudip@gmail.com Ankit Gupta Assistant Professor Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi , U.P. India, Mob No: ; anki_ce11@yahoo.co.in Word count: 3,189 words text + 17 tables/figures x 250 words (each) = 7,439 words Submission Date:

2 Abstract: Rapid urbanization increases the number of vehicles on a road section significantly throughout the country. Indian traffic is purely heterogeneous consisting of variety of vehicles which comprise of wide range of static and dynamic characteristics. To estimate volume of such heterogeneous traffic it is essential to convert the different types of vehicles into equivalent passenger cars and express the volume in terms of Passenger Car Unit (PCU) per hour. The equivalency unit is universally adopted for measurement of traffic volume and the value is obtained by taking the passenger car as the Standard Vehicle. The present study has been concentrated on four and six lane divided urban arterials in Kolkata. The PCU of vehicles have been presented for both categories of urban roads and the values are found to be higher than the values given in IRC: It has also been observed that, PCU values of different types of vehicles have been found to be different for different ranges of traffic volume due to its dynamic characteristics. Capacity of four lane and six lane divided urban arterials have been found to be 4465 PCU/hr and 6264 PCU/hr using the dynamic PCU values fitted by Greenshield s model. A mathematical model has been developed on the basis of the variation of PCU with traffic volume. The model is developed to forecast the PCU for several vehicular categories that has been statistically validated at different ranges of traffic volume. Keywords: Capacity, Heterogeneous Traffic, Passenger Car Unit, Traffic Volume, Urban Arterials.

3 INTRODUCTION India is a developing country with a great network that makes it the second largest road network in the world. Urban arterials serve the major traffic carrying corridor in a metropolitan city. Rapid urbanization increases the number of vehicles on a road section significantly throughout the country. But the traffic in India is purely heterogeneous. The traffic stream in India has variety of vehicles like cars, heavy vehicles such as buses, trucks, light commercial vehicles, motorized two wheelers and three wheelers and non-motorized vehicles which comprise of wide range of static and dynamic characteristics. The analysis of such heterogeneous traffic stream is simple if the relative effect of each vehicle type can be expressed in terms of some common units. The PCU or PCE is the universally adopted unit of measurement of traffic volume or capacity and the value is derived by taking the passenger car as the Standard Vehicle. The estimation of PCU of different categories of vehicles is also necessary for design of different traffic facilities, operational analysis of roadway facilities, management of traffic regulation and control of traffic. OVERVIEW OF PREVIOUS LITERATURES There are many studies available in literature to estimate the PCU of different categories of vehicles under heterogeneous traffic conditions in India and other countries. The term Passenger Car Unit (PCU) was first introduced in the 1965 US HCM (TRB) (1) and reported for grades of specific length and percent, proportion of trucks, and LOS (A-E). In the HCM 2000 (2), passenger car equivalent (PCE) is defined as the number of passenger cars displaced by a single heavy vehicle of a particular type under specified roadway, traffic, and control conditions. Chandra and Kumar (2003) (3) studied the effect of lane width on PCU values and also on the capacity of a two-lane road under mixed traffic conditions. PCUs were estimated at ten road sections for nine categories of vehicles. They found that PCU for a vehicle type increases linearly with the width of carriageway. Al-Kaisy et al. (2005) (4) worked on developing PCE factor for heavy vehicles during congestion. A set of PCE factors for oversaturated traffic conditions was developed for use in traffic analyses. Basu et al. (2006) (5) worked on PCE at an urban midblock using stream speed They studied the impact of traffic volume and its composition on PCE of different categories of vehicle in a mixed traffic stream of an urban midblock section. PCE values were found to increase with increase in traffic volume. However, the effect of traffic volume on PCE was predominant for heavy vehicles. It was found for heavy vehicles and new technology cars that PCE values increased with an increase in compositional share of respective vehicle types in the traffic stream. Zhang et al. (2006) (6) proposed PCE for different categories of vehicles using vehicle moving space as a measure to derive PCEs. The data on two lane and four lane highways in china were used to estimate PCE under different roadway and level of service conditions. The authors suggested that PCE values increase with number of lanes and LOS A to E. Rakha et al. (2007) (7) estimated the truck equivalency factor for freeway sections at different grades. PCEs are developed for broader range of vehicle weight to power ratio in the INTEGRATION software using HCM procedure. The authors estimated PCE for truck at different LOS and 2 to 5 percent grades, when their proportion in the mix is more than 25 percent which was beyond the limit of HCM Cao and Sano (2012) (8) worked on estimating capacity and motorcycle equivalent units on urban roads in Hanoi, Vietnam. Nonlinear regression analysis was employed to calculate the mean effective space for particular type of vehicle from the correlation between the effective spaces of subject vehicle and the speed of motorcycle in the front of the subject vehicle.

4 Satyanarayana et al. (2012) (9) worked on development of PCU factors and capacity norms at midblocks of rural highways in Visakhapatnam. Dhamaniya and Chandra (2013) (10) worked on the concept of stream equivalency factors for heterogeneous traffic on urban arterial roads. They converted heterogeneous traffic volume in vehicle per hour to homogeneous PCU per hour without determining PCU factors for each and every individual vehicles type by making use of stream equivalency factors (SEF). Paul and Sarkar (2013) (11) determined dynamic PCU of different types of vehicles on urban roads. The effect of proportion of Non-Motorized Traffic (NMT) and heavy vehicles on PCU of different categories of vehicles were studied and the effect of stream speed on PCU was presented in form of mathematical equations. Khode et al. (2014) (12) studied on impact of lane width of road on passenger car unit capacity under mix traffic condition in cities on congested highways. It was found that the PCU for a vehicle type increases with increasing lane width. Muhammad Adnan (2014) (13) studied on passenger car equivalent in heterogeneous traffic environment. Four different methods were used to estimate the PCU of vehicles. They found that method that incorporate vehicles speed along with projected area of vehicles were provide appropriate estimate of PCE values. Dhamaniya and Chandra (2014) (14) worked on midblock capacity of urban arterial roads in India. They considered the speed and size of the vehicle as the prime variables for determination of PCU. The variation in PCU for different types of vehicles was established graphically. OBJECTIVE AND STUDY AREA This study has been concentrated on finding the roadway capacity and PCU values for different categories of vehicles under heterogeneous traffic conditions on mid-block section in urban arterials and also to analyse the variation of PCU values with respect to different ranges of traffic volume for urban arterials. Two study sections have been selected on four lane divided urban arterial and one study section on six lane divided urban arterial based on various criterion such as, the section should have wide variation in proportion of different categories of vehicles, free from the effects of road side friction, intersection, parking facilities, bus stop, pedestrian movements, curvature, gradient and median opening etc. the selected study sections have been shown in Figure 1. A straight mid block section of the selected urban roads has been selected for the data collection purpose. The details of study sections are shown in Table 1. TABLE 1 Details of Study Sections Study Sections Carriageway Width (m) Road Geometry VIP Road 10.8 Six lane divided C/W Kona Arterial Road Four lane divided C/W E. M. Bypass 7 Four lane divided C/W

5 FIGURE 1 Location of the selected study sections RESEARCH METHODOLOGY The present study has adopted Chandra s method (REF) to estimate the PCU values of vehicles. In a heterogeneous traffic stream, speed of the vehicles is mostly affected among the other traffic stream parameters. In Chandra s method speed is considered as the basic parameter for determination of PCU. Hence, Chandra s method has been adopted as proposed methodology. In this study Standard Car (CS) is considered as the standard design vehicle. According to Chandra s method PCU of any vehicle type can be obtained by using the following relationship. PCUi (Vc/Vi )/(Ac/Ai ) In equation 1, the variables Vc and Vi denotes the mean speed of standard car and vehicle type i respectively and Ac and Ai denotes their respective projected rectangular area. The numerator in the above equation is the function of volume of traffic stream as the speed of any vehicle type depends upon its category, own volume and volume of other vehicles. Therefore, speed of any vehicle type is true representation of overall interaction of a vehicle type due to presence of other vehicle of its own category and of other types. The denominator represents the carriageway occupancy with respect to standard car. The physical size of different types of vehicles have been adopted from the Chandra and Kumar (2003). (1)

6 TABLE 2 Vehicle Categories and Their Average Dimensions (Chandra and Kumar, 2003) Category Average Dimension Length(m) Width (m) Projected rectangular area on ground (m 2 ) Standard Car (Maruti800, Alto etc.) Big Car (Honda city, Skoda etc.) Truck LCV Two Wheeler Three Wheeler FIELD DATA COLLECTION Field data have been collected on a weekday during 8 a.m. to 8 p.m. at Kona arterials and VIP road and during 8 a.m. to 1 p.m. at E.M.Bypass in Kolkata to determine the traffic volume, speed of different types of vehicles and composition of traffic stream. Video photography technique has been used to record the movements of vehicular traffic in one direction of travel by considering a trap length of 60 m for the determination of traffic volume and composition of the traffic stream. Tru- speed laser gun has been used to collect the spot speed data of different categories of vehicles travelling in the trap length for a sample size of 50%. DATA EXTRACTION AND PROCESSING The collected field data have been brought to a work station to extract different traffic stream parameters such as: Classified Traffic Volume and Composition Classified traffic volume count has been carried out by playing the recorded video at the work station. All vehicles in the traffic stream have been grouped and divided into seven categories. The classified vehicle count has been done manually at every 5 minutes interval and it has been converted into hourly traffic volume.

7 Hourly Volume (veh/hr) Hourly Volume (veh/hr) Hourly Volume (veh/hr) Mondal, Chakraborty, Roy, Gupta :35-8:35 a.m :40-10:40 a.m :00-1:56-12:00 2:56 a.m. p.m. Time of Day 5:27-6:27 p.m :32-7:32 p.m. 10:30-11:30 a.m. 11:30-12:30 p.m. Time of Day 6% LCV 13% LCV 3% 4% M3W Truck 4% 6% CB 36% CB 26% Truck 2% M3W 3% TW 25% CS 20% TW 21% CS 31% Two Wheeler Standard Car Big Car Light Commercial Vehicle Truck Motorized Three Wheeler FIGURE 2 Hourly variation of traffic volume and proportion of individual vehicle on Kona Arterial :30-11:30 a.m :30-12:30 p.m :00-2:00 p.m. 2:00-3:00 p.m. Time of Day 3:15-4:15 p.m. 4:15-5:15 p.m. 6% LCV 4% CB 23% M3W 7% TW 24% CS 36% Two Wheeler Standard Car Big Car Light Commercial Vehicle Truck Motorized Three Wheeler FIGURE 3 Hourly variation of traffic volume and proportion of individual vehicle on E. M. Bypass Two Wheeler Standard Car Big Car Light Commercial Vehicle Motorized Three Wheeler FIGURE 4 Hourly variation of traffic volume and proportion of individual vehicle on VIP road

8 Vehicular Speed (km/hr) Vehicular Speed (kmph) Mondal, Chakraborty, Roy, Gupta Speed Data The time mean speed of the individual vehicle has been obtained by using the laser gun technique. The obtained time mean speed has been converted into space mean speed by using the relationship between time mean speed and space mean speed. DATA ANALYSIS Variation in Speed with Traffic Volume The variation in speed of individual vehicle type has been studied by determining the speed of individual vehicle type at different volume levels shown in Figure 5 and Figure 6 respectively. Standard Car Big Car Two Wheeler Motorized Three Wheeler Light Commercial Vehicle 70 Truck Volume (veh/hr) FIGURE 5 Variation in speed of individual vehicle with traffic volume on four lane divided road Standard Car Big Car Two Wheeler Motorized Three Wheeler Light Commercial Vehicle Volume (veh/hr) FIGURE 6 Variation in speed of individual vehicle with traffic volume on six lane divided road

9 From the Figure 5 and 6, a flatter speed variation is observed for small size vehicle with traffic volume due their higher maneuverability characteristics whereas a steep speed variation is observed for large size vehicle with traffic volume due to their lesser maneuverability characteristics with respect to standard car. Estimation of PCU Values In order to develop a proper speed flow equation to estimate capacity and design of traffic facilities for heterogeneous traffic, it is necessary to convert the heterogeneous traffic into homogeneous by using a common unit, which is termed as Passenger Car Unit. Chandra s model has been used to determine the PCU of different vehicle categories. It has been observed that speed of the individual vehicle class is different at different volume levels. So, a range of PCU values of different categories of vehicles have been determined for limited range of traffic volume for both urban roads and presented in the Table 3 and Table 4. TABLE 3 PCU of Different Categories of Vehicles for Four Lane Divided Urban Arterial Traffic volume (veh/hr) Type of Vehicle > Min Max Min Max Min Max Min Max Big Car (CB) Two Wheeler (TW) Motorized Three Wheeler (M3W) LCV Truck TABLE 4 PCU of Different Categories of Vehicles for Six Lane Divided Urban Arterial Traffic volume (veh/hr) Type of Vehicle Min Max Min Max Min Max Min Max Big Car (CB) Two Wheeler (TW) Motorized Three Wheeler (M3W) LCV Estimation of Capacity This study has been adopted the Greenshield s model for estimation of capacity by considering the average PCU value of individual vehicle type on four lane and six lane divided urban arterials. Therefore, the speed flow model which follows the parabolic relationship, developed by using

10 Speed (km/hr) Speed (km/hr) Mondal, Chakraborty, Roy, Gupta regression technique has been considered. A scatter diagram has been developed by plotting the speed and flow of the urban arterials shown in Figure 7 and Figure 8 respectively Greenshield's Model Field Data u = k , R² = 0.8 q= *u 8.864*u Flow (PCU/hr) FIGURE 7 Speed-flow relationship for four lane divided urban arterial Greenshield's Model Field Data u = k , R² = 0.89 q= *u 6.021*u Flow (PCU/hr) FIGURE 8 Speed-flow relationship for six lane divided urban arterial It has been observed that the speed flow curve fits well with the observed data, indicating the validity of the field data for highly heterogeneous traffic flow. The capacity of four lane and six lane divided urban arterial, under heterogeneous traffic conditions is estimated as about 4465 PCU/hour and 6264 PCU/hour respectively.

11 PCU value PCU value PCU value PCU value Mondal, Chakraborty, Roy, Gupta Variation of PCU at Different Traffic Volume on Four and Six Lane Divided Urban Arterials The effect of traffic volume on PCU has been studied by determining the PCU of different types of vehicles at different volume levels. It is observed from Figure 9 that PCUs of large size vehicles such as CB, LCV, and Truck are increasing and for small size vehicles such as TW and M3W, it is decreasing with increase in traffic volume. In a road section, as the traffic volume increases corresponding density will increase. So, the vehicles will move at a lower speed. Large vehicles require more space to move in traffic stream and also have less maneuverability with respect to CS while small size vehicles like 2W and M3W require less space and move to any lateral space available between any large size vehicles in the traffic stream with better maneuverability respect to CS. Therefore, the speeds of the small size vehicles are not affected by the increase in traffic volume. So, the speed difference between CS and small size vehicle decreases and for larger vehicles increases and corresponding PCU values for CB, LCV, and Truck increases and for 2W and M3W it decreases. It is also observed from Figure 10 that variation of PCU values of different categories of vehicles with traffic volume on six lane road follows the same trend as it is on four lane road. PCUs of large size vehicles such as CB, LCV and are increasing and for small size vehicles such as TW and M3W, it is decreasing with increase in traffic volume. CB LCV Truck TW M3W Traffic Volume (veh/hr) Traffic Volume (veh/hr) FIGURE 9 Variation in PCU of CB, LCV, Truck,, TW and M3W with traffic volume on 4 lane road CB LCV Traffic Volume (veh/hr) TW M3W Traffic Volume (veh/hr) FIGURE 10 Variation in PCU of CB, LCV,, TW and M3W with traffic volume on 6 lane road

12 The PCU of different categories of vehicles has been found to change linearly with increase in traffic volume. A mathematical model has been developed based on variation of PCU values at different volume levels for different categories of vehicles. The general form of the model is given in the following equation. b PCU a 10 (2) ( ) q c Where, q = Traffic Volume in veh/hr a, b, and c = Constant The value of constants for different categories of vehicles for both urban arterial are given in the Table 5. TABLE 5 Values of Constants for Four and Six Lane Divided Urban Arterial Four Lane Road Six Lane Road Vehicle Category a b c a b c Big Car LCV Truck TW M3W VALIDATION OF PCU VALUES AND MODEL In order to check the accuracy of the PCU values and model for both the urban roads, field data have been collected at one more four lane and six lane divided urban arterial. Required data have been extracted from the collected field data. The obtained model has been used to estimate the PCU values of the newly selected study sections. Therefore, the suggested (given in Table 3 and 4) and estimated PCU values of vehicles on four lane and six lane divided urban arterials have been compared to observe the statistical significance between these two sets of PCU values using statistical two tail t-test. Tables 6 and 7 shows the result of t-test performed for comparing mean PCU of different categories of vehicles. The calculated value of t-statistics against the critical value at 95% level of confidence is higher for every categories of vehicles. It implies that there is no significant difference between suggested and estimated PCU values of vehicles which further validated the obtained mathematical model.

13 TABLE 6 Results of t-test for Comparing PCU of Vehicles on Four Lane Divided Urban Road with Traffic Volume Vehicle Type Mean t0.05 tcritical (two-tailed) Remarks Big Car Suggested Value 1.52 Estimated Value Truck Suggested Value 4.55 Estimated Value Suggested Value There is no Estimated Value 5.18 significant Suggested Value 2.84 LCV difference Estimated Value 2.82 TW Suggested Value 0.25 Estimated Value M3W Suggested Value 1.16 Estimated Value TABLE 7 Results of t-test for Comparing PCU of Vehicles on Six Lane Divided Urban Road with Traffic Volume Big Car LCV TW M3W CONCLUSION Vehicle Type Mean t0.05 Suggested Value 1.56 Estimated Value 1.57 Suggested Value 5.24 Estimated Value 5.36 Suggested Value 2.72 Estimated Value Suggested Value Estimated Value Suggested Value 1.01 Estimated Value tcritical (twotailed) Remarks There is no significant difference In the present study the dynamic PCUs have been estimated for both the urban arterials. This study considers speed and size of the vehicle as prime variables for estimation of PCU factors. The calculated PCUs of vehicles are larger than the values given in IRC: A range of PCU values have been estimated for each category of vehicles for limited range of traffic volume. Capacity of four lane and six lane divided urban arterials have been found to be 4465 PCU/hr and 6264 PCU/hr using the Greenshield s model. IRC 106:1990 suggests the capacity of four lane and six lane divided urban road as 3600 PCU/hr and 5400 PCU/hr. This variation is obtained due to higher operating speed and higher PCU values of vehicles. It has also been found that per lane capacity decreases as the number of lane increases. The PCU value of Big Car, LCV, and Truck increases linearly with the increase in traffic volume but for Two Wheeler and Motorized Three Wheeler it decreases linearly with increase in traffic volume. A mathematical model has

14 been developed based on the variation of PCU values. Two tail t-test has been used for the validation of the suggested PCU values. It has been observed that the calculated value of t-statistics against the critical value at 95% level of confidence is higher for every category of vehicles. It implies that, there is no significant difference between suggested and estimated PCU values of vehicles which further validated the obtained mathematical relationship.

15 REFERENCES 1. Highway Capacity Manual TRB, National Research Council, Washington D.C, Highway Capacity Manual Washington, D.C. Transportation Research Board, National Research Council, Chandra, S. and Kumar, U. Effect of Lane Width on Capacity under Mixed Traffic Conditions in India. Journal of Transportation Engineering, ASCE, Vol. 129, No. 2, 2003, pp Al. Kaisy, A. F., Jung, Y. and Rakha, H. Developing Passenger Car Equivalency Factors for Heavy Vehicles during Congestion. Journal of Transportation Engineering, ASCE, Vol. 131, No. 7, 2005, pp Basu, D., Roy, S. M. and Maitra, B. Modelling Passenger Car Equivalency at an Urban Mid-Block using Stream Speed as Measure of Equivalence. Journal of European Transport/ Transporti Europei, Issue. 34, 2006, PP-(75-87). 6. Zhang, J.W., Dai, W.M. and Xiugang Li. Developing Passenger Car Equivalents for China Highways based on Vehicle Moving Space, Transportation Research Board Annual meeting, Paper No. 1562, Rakha, H., Ingle, A., Hancock, K. and Al-Kaisy, A. Estimating Truck Equivalencies for Freeway Sections. Transportation research Record: Journal of the Transportation research Board, No. 2027, Transportation Research Board of the National Academics, Washington, D.C., 2007, pp Cao, N. Y. and Sano, K. Estimating Capacity and Motorcycle Equivalent Units on Urban Roads in Hanoi, Vietnam / (ASCE) TE Journal of Transportation Engineering, ASCE, June Satyanarayana, P., Durga R. K. and Gopala R. Development of PCU Factors and Capacity Norms at Mid Blocks of Rural Highways in Visakhapatnam. Indian journal for Education and Information Management, ISEE, Vol.1 (5), Dhamaniya, A., and Chandra, S. Concept of Stream Equivalency Factor for Heterogeneous Traffic on Urban Arterial Roads. Journal of Transportation Engineering, ASCE, Vol.139, 2013, pp Paul, P. and Sarkar, P. Determination of Dynamic PCUs of Different Types of Passenger Vehicles on Urban Roads: A Case Study, Delhi Urban Area. Indian Highways, 41(4), Indian Roads Congress, New Delhi, Khanorkar, A. R., Ghodmare, S.D. and Dr. Khode, B.V. Impact of Lane Width of Road on Passenger Car Unit Capacity under Mix Traffic Condition in Cities on Congested Highways. International Journal of Engineering Research and Applications. ISSN: , Vol. 4, Issue 5(Version 2), May 2014, pp Adnan, M. Passenger Car Equivalent Factors in Heterogeneous Traffic Environment-are we using the Right Numbers?. Procedia Engineering 77 (2014) , Fourth International Symposium on Infrastructure Engineering in Developing Countries, IEDC Dhamaniya, A. and Chandra, S. Midblock capacity of urban arterial roads in India. Indian Highways, Vol. 42, No. 7, 2014, pp , July Chandra, S. Capacity estimation procedure for two lane roads under mixed traffic conditions. Journal of Indian Road Congress, 165, pp