Development of Professional Driver Adjustment Factors for the Capacity Analysis of Signalized Intersections

TECHNICAL NOTES Development of Professional Driver Adjustment Factors for the Capacity Analysis of Signalized Intersections M. Mizanur Rahman 1 ; Tanweer Hasan 2 ; and Fumihiko Nakamura 3 Abstract: Various factors are provided in the Highway Capacity Manual to adjust the base saturation flow rate for the capacity analysis of signalized intersections under the prevailing conditions. There are, however, no factors to account for the possible change of capacity at signalized intersections caused by professional taxi drivers in the traffic stream. The results obtained from a study to develop professional driver adjustment factors for capacity analysis of signalized intersections are summarized. The factors were derived on the basis of the data collected in Yokohama City, Japan. The results indicated that taxi drivers had a significant impact on the saturation flow rate. When a signalized intersection was identified with a high volume of taxi drivers, the saturation flow rate as well as the capacity could be increased by 20%, which corresponded to a professional driver adjustment factor as high as 1.20. DOI: 10.1061/ ASCE 0733-947X 2008 134:12 532 CE Database subject headings: Traffic flow; Flow rate; Intersections; Traffic signals; Driver behavior. Introduction The capacity of a signalized intersection is commonly determined relative to a theoretical value that is typically applicable only under ideal conditions. Because these conditions are difficult, if not impossible, to meet in most cases, planners and engineers must know which factors might restrict intersection capacity and to what extent. Adjustment factors affecting the signalized intersection capacity are classified as geometric, traffic, operational, environmental, and driver population Stokes 1989. These adjustment factors are established on the basis of field studies Zegeer 1986; McCoy and Heimann 1990. Except for driver population factors, many other adjustment factors have been thoroughly studied and well addressed in the Highway Capacity Manual Zhou et al. 2000; Transportation Research Board 2000. Although general agreement exists on the effects of certain physical factors on capacity, considerable controversy exists on the effects of various external factors on capacity. These external factors seem to be site specific, or at least local, in nature. Perhaps the most significant of these external factors requiring additional research is the role of driving behavior in intersection capacity Stokes 1989. 1 Assistant Professor, Dept. of Civil Engineering, Bangladesh Univ. of Engineering and Technology BUET, Dhaka 1000, Bangladesh. E-mail: mizanur@ce.buet.ac.bd 2 Associate Professor, Dept. of Civil Engineering, Bangladesh Univ. of Engineering and Technology BUET, Dhaka 1000, Bangladesh. E-mail: tanweer@ce.buet.ac.bd, tanweer20@gmail.com 3 Professor, Dept. of Civil Engineering, Yokohama National Univ., 79-5, Tokiwadai, Hodogaya-ku, Yokohama 240, Japan. E-mail: nakamura@cvg.ynu.ac.jp Note. Discussion open until May 1, 2009. Separate discussions must be submitted for individual papers. The manuscript for this technical note was submitted for review and possible publication on April 2, 2007; approved on June 20, 2008. This technical note is part of the Journal of Transportation Engineering, Vol. 134, No. 12, December 1, 2008. ASCE, ISSN 0733-947X/2008/12-532 536/$25.00. The basic driving task consists of three performance levels control, guidance, and navigation AASHTO 2004. At the guidance and navigation levels, information handling is increasingly complex, and drivers need more processing time to make decisions and respond to information inputs Alexander and Lunenfeld 1986. However, the complexity levels among drivers during guidance and navigation phases may vary depending on their familiarity with the areas they use to drive, which in turn may have significant effects on the intersection capacity. For example, a high percentage of unfamiliar or nonlocal drivers in the traffic stream may reduce the capacity of a signalized intersection by 19% Zhou et al. 2000. This is because the nonlocal drivers experience significantly higher start-up delay, can less efficiently use the yellow time, and maintain significantly higher saturation headway. The previous studies also indicated that any driver population groups other than weekday commuters would utilize freeways less efficiently and that when there are a large number of nonlocal drivers in the freeway traffic, capacity was significantly reduced Lu et al. 1997; Brilon and Ponzlet 1996; Sharma 1994; Sharma 1987. This study is also about determining driver population factors for the capacity analysis of signalized intersections. However, it deals with taxi drivers, who are very different from nonlocal drivers. drivers are professional and quite familiar with the areas they use to drive. It is also reasonable to assume that their driving behaviors for example, car-following, lane changing, and maintaining a shorter gap from the lead vehicle while in a queue are different as compared to those of the commuters or other nonprofessional drivers. Although researchers have investigated the impacts of taxi traffic on the capacity of urban road sections Golias 2003, a thorough review of the literature reveals that their impacts on the capacity of signalized intersections are yet to be quantified. Traffic data collected at eight different signalized intersection approaches in Yokohama City, Japan are analyzed to develop adjustment factors for the capacity analysis of signalized 532 / JOURNAL OF TRANSPORTATION ENGINEERING ASCE / DECEMBER 2008

Table 1. Characteristics of Data Collection Sites Study site Signal type Green time of phase s Speed limit km/h Number of lanes Lane position Number of cycles observed Average queue length vehicle PC a PC and taxi PC PC and taxi 1 Pretimed 72 30 4 Middle 18 26 10.43 9.35 2 Pretimed 84 50 3 Middle 21 32 9.86 12.50 3 Pretimed 84 50 3 Middle 27 35 11.12 11.69 4 Pretimed 78 50 3 Middle 23 33 8.64 8.23 5 Pretimed 80 40 2 Inner 19 26 11.42 13.06 6 Pretimed 80 50 2 Outer 14 19 10.07 8.78 7 Pretimed 84 50 2 Inner 18 24 12.23 13.21 8 Pretimed 78 40 3 Outer 20 35 11.21 12.39 Total number of cycles 160 230 a. intersections when there are large percentages of taxi drivers in the traffic stream. Methodology The capacity parameter considered in this study is the saturation headway. The average or mean headways determined for different positions of passenger cars in the queue are compared with those obtained for the corresponding positions of taxis in the queue to see if they are significantly different. Then analysis of variance ANOVA is carried out for different levels of taxi drivers in the traffic stream to test the null hypothesis that saturation headways for different groups are equal. The results are then extended to develop regression models relating saturation headway with proportion of taxi drivers. Finally, professional driver adjustment factors are determined in such a way so that they become compatible with and applicable to the Highway Capacity Manual. Data Collection and Processing Eight approaches of six signalized intersections were selected for this study. All data collection sites were located in Yokohama City of Kanagawa prefecture of Japan. They all were near different rail stations in the downtown area. They were carefully selected so that there were no obvious deficiencies of roadway or traffic conditions that would affect the capacity of signalized intersections. The following criteria were used in the selection of study sites: high traffic volume, level terrain, higher proportion of taxis, no parking allowed, and insignificant disturbance from bus stops. The characteristics of the data collection sites and volume of observed data are shown in Table 1. Data were collected for through movements only. Traffic data at the study sites were collected by using a portable digital video camera system. The videotaping of traffic movements was conducted from August to October of 2002. All data were collected during morning peak periods or evening peak periods. Within the filming period, only interested lane traffic movement data were recorded. More than 14 h of traffic data were recorded for this study. The tapes were first examined in the laboratory to screen out cases that were not suitable for this study. The platoons containing unimpeded, straight-through passenger cars and taxis stopped before entering an intersection were considered as valid cases for the study. The valid cases were later viewed on a television screen to extract the entering headways of queued vehicles. Time Code reader software was used to estimate the headways of vehicle entering the intersections. This software can calculate the entering headways with 1/30 s accuracy. The entering headway of the first vehicle in a queue was taken to be the time elapsed between the start of a green indication and the time when the rear bumper of the vehicle cleared the stop line. For other vehicles in the queue, the entering headways were taken to be the elapsed time, rear bumper to rear bumper, as successive vehicles passed an intersection stop line. From the data reduction phase, a total of 390 single lane vehicular platoons 390 cycles were found to be valid for this study see Table 1. These headway data were later used to calculate the saturation flow rate and to develop the adjustment factors. Effect of the Position of on Mean Headway Time headway between vehicles being discharged from a queue at a signalized intersections is a measure of the intersection s capacity. The headways during saturation flow are related to the size of the vehicles and it is found that vehicles follow a small car with a closer headway than a full-sized car and a small car follows a vehicle closer than a full-sized car Steuart and Shin 1978. However, this study indicated that, between two full-sized cars, the headway values varied depending on whether the vehicle was a passenger car or a taxi. It is, however, worthy to note that there was no meaningful difference between passenger cars and taxis in terms of their size, shape, and engine capacity 1,300 1,500 cm 3 motor cars. The headway values collected from the study sites were analyzed according to the headway classification shown in Fig. 1. The headways were classified as: 1 H TT =headway of a taxi following a taxi; 2 H PT =headway of a taxi following a passenger car; 3 H PP =headway of a passenger car following a passenger car, and 4 H TP =headway of a passenger car following a taxi. The results are shown in Table 2. It can be seen from Table 2 that the headway of a taxi is the smallest when it is following a taxi H TT. The headway of a taxi following a passenger car, H PT, was found to be a little larger than H TT, but smaller than the headway of a passenger car following a passenger car, H PP see Table 2. When a taxi is the leader of a queue, its headway is smaller than a passenger car. It is also true for other positions in the queue. Fig. 2 shows the effect of the presence of taxis on the JOURNAL OF TRANSPORTATION ENGINEERING ASCE / DECEMBER 2008 / 533

Hpp HPT HTP HTT Saturation headways (sec) 1.9 1.85 1.8 1.75 1.7 1.65 y = -0.0032x+ 1.8909 R 2 = 0.8904 Fig. 1. Headway classification used in the study headway values of the queued vehicles for different queue positions. As can be seen from Fig. 2, a significant difference at 95% confidence level between the average headways of passenger cars and taxis occurs at the beginning of the queue. The significant difference between headway values for the lead vehicles in the queue suggested that capacity improvements are possible with the presence of taxis. In addition, these improvements would be compounded each time the queue comes to a stop. The average headway of the first vehicle in the queue is 3.24 s for a passenger car and 2.98 s for a taxi. A test of the hypothesis that these mean values are equal, is not accepted at 95% confidence level, which establishes that the average headways are significantly different. Development of Saturation Flow Adjustment Factors ANOVA was performed to examine whether the impact of the proportion of taxi drivers on the capacity parameter saturation Table 2. Results of Headway Analysis Queue position H TT s H PT s H PP s H TP s 2 2.26 2.32 2.55 2.42 3 2.11 2.16 2.31 2.23 4 1.98 2.04 2.10 2.08 5 1.91 1.94 1.96 1.95 6 1.86 1.88 1.93 1.89 7 1.83 1.85 1.90 1.87 8 1.81 1.86 1.87 1.85 9 1.76 1.79 1.83 1.81 10 1.73 1.76 1.81 1.78 Note: H TT =headway of a taxi following a taxi; H PT =headway of a taxi following a passenger car; H PP =headway of a passenger car following a passenger car; and H TP =headway of a passenger car following a taxi. Average headways (sec) 3.5 3.3 3.1 2.9 2.7 2.5 2.3 2.1 1.9 1.7 1.5 1 2 3 4 5 6 7 8 9 10 11 12 Position of the vehicle in queue Fig. 2. Effects of taxi on the headways of queued vehicles 1.6 0 10 20 30 40 50 60 70 80 Proportion of taxi (%) Fig. 3. Relationship between saturation headways and proportion of taxis headway was significant. In order to perform the ANOVA, the proportion of taxi driver levels was divided into various groups. The null hypothesis, H 0, was that the capacity parameter saturation headway in all the taxi driver groups was equal. The results of the ANOVA for through traffic indicated that the impact of taxi drivers on the saturation headway was statistically significant at 95% confidence level F value of 17.98 versus F critical of 2.35; null hypothesis rejected. This implies that the higher proportion of taxis decreases the saturation headway and consequently increases the capacity of signalized intersections. Saturation flow rate is the reciprocal of saturation headway. In the Highway Capacity Manual, saturation headway is calculated by averaging the discharging headway from the fifth queued vehicle to the last queued vehicle as shown in the following: H s = i=1 m ni j5 H ij m 1 i=1 n i 4 where H s =saturation headway s ; H ij =discharge headway of jth queued vehicle in cycle i s ; n i =number of vehicles in queue of cycle i, n i 4; and m=total number of cycles during an observation period. In order to make the research findings compatible with and applicable to the Highway Capacity Manual, saturation headways were estimated using Eq. 1 from the observed data for various proportions of taxis in the traffic stream. Saturation headways and the corresponding proportions of taxis were plotted see Fig. 3. As can be expected, they showed a negative linear relationship. To establish the relationship between the saturation headway H s s and proportion of taxi T %, regression analysis was performed assuming H s = a + bt 2 The results of the regression analysis indicated that the model has a high coefficient of determination, R 2, of 0.89. The model as well the coefficients a =1.8909 and b = 0.0032 are significant at 95% confidence level t critical =1.65 for the data set; t a =267.66; t b = 17.79; F=317. The signs of the coefficients are also logical as the saturation headway decreases with increase of proportion of taxis in the traffic stream. The saturation headways H s for different proportions of taxis were calculated and are shown in Table 3. On the basis of the capacity analysis procedure of the Highway Capacity Manual, the saturation flow rate of a given lane group can be expressed as follows: 534 / JOURNAL OF TRANSPORTATION ENGINEERING ASCE / DECEMBER 2008

Table 3. Saturation Headway for Different Proportions of s Proportion of taxis % S prevailing = S base F other f taxis 3 where s prevailing =saturation flow rate under prevailing conditions pcphgpl ; S base =saturation flow rate under ideal conditions pcphgpl ; F other =combination of all other adjustment factors except proportion of taxis; and f taxi =adjustment factors for taxis. In the Highway Capacity Manual, adjustment factors are developed by dividing the prevailing saturation flow rate by the ideal saturation flow rate Zhou et al. 2000. Thus, adjustment factors for taxis, f taxis were estimated as follows: f taxis = S prevailing S base = H pc 0% taxis H taxis Saturation headway s 0 1.89 5 1.87 10 1.86 15 1.84 20 1.82 30 1.79 40 1.76 50 1.73 60 1.69 70 1.66 80 1.63 90 1.60 100 1.57 where f taxis =adjustment factors for taxis; H pc 0% taxis =saturation headway with 0% taxis; and H taxis =saturation headway with a given proportion of taxis. The saturation headway values shown in Table 3 for different proportions of taxis were used in Eq. 4 to calculate the adjustment factors. The adjustment factors are presented in Table 4. As can be seen from Table 4, when the proportion of taxis increased from 0 to 100%, saturation headway decreases by 20%. Other factors remaining constant, this reduction in headway value Table 4. Adjustment Factors for Different Proportions of s Proportion of taxis % Adjustment factor f taxis 0 1.00 5 1.01 10 1.02 15 1.03 20 1.04 30 1.05 40 1.07 50 1.09 60 1.11 70 1.13 80 1.16 90 1.18 100 1.20 4 would increase the capacity of a signalized intersection by the same amount. The f taxi values presented in Table 4 can be used with other adjustment factors in the Highway Capacity Manual to perform capacity analysis of signalized intersections with higher proportions of taxis. Conclusions A procedure for developing professional driver adjustment factors for capacity analysis of signalized intersections is presented. Only through movements of vehicles were considered. It was found that the taxi drivers had a significant impact on the saturation flow rate and capacity of signalized intersections. The study results indicated that the capacity of a signalized intersection could be increased by 20% when the proportion of taxi drivers increased from 0 to 100%. This corresponds to an adjustment factor of as high as 1.20. Results obtained from this study may help transportation practitioners to perform capacity analysis of signalized intersections more efficiently, especially for the locations with a large percentage of taxi drivers. The Highway Capacity Manual Transportation Research Board 2000 assumes that the signalized intersections in central business districts are relatively inefficient as compared to those in other locations and provides an adjustment factor of 0.9 for such areas. The results from this study, however, indicate that the signalized intersections in central business districts may not be inefficient in discharging traffic through the intersection as the central business districts usually have high percentages of taxi traffic. Notation The following symbols are used in this technical note: F other combination of all other adjustment factors except proportion of taxis; f taxis adjustment factors for taxis; H ij discharge headway of jth queued vehicle in cycle i s ; H pc 0% taxis saturation headway with 0% taxis; H PP headway of a passenger car following a passenger car; H PT headway of a taxi following a passenger car; H s saturation headway s ; H taxis saturation headway with a given proportion of taxis; H TP headway of a passenger car following a taxi; H TT headway of a taxi following a taxis; m total number of cycles during an observation period; n i number of vehicles in queue of cycle i; S base saturation flow rate under ideal conditions pcphgpl ; S prevailin saturation flow rate under prevailing conditions pcphgpl ; and T proportion of taxis in the traffic stream %. References Alexander, G. J., and Lunenfeld, H. 1986. Driver expectancy in highway design and traffic operations. Rep. No. FHWA-TO-86 1, U.S. JOURNAL OF TRANSPORTATION ENGINEERING ASCE / DECEMBER 2008 / 535

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