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Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions
Transportation Research Part F 60 (2019) 111–120
Contents lists available at ScienceDirect
Transportation Research Part F journal homepage: www.elsevier.com/locate/trf
Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions Bharat Kumar Pathivada ⇑, Vedagiri Perumal Department of Civil Engineering, Indian Institute of Technology (IIT) Bombay, Mumbai 400076, India
a r t i c l e
i n f o
Article history: Received 5 February 2018 Received in revised form 13 October 2018 Accepted 14 October 2018
Keywords: Driver behavior Dilemma zone Yellow signal Signalized Intersection Mixed traffic conditions Stop/Go decisions
a b s t r a c t Intersections are important node points in the road network, ensuring safe and efficient way of maneuvering the traffic. The Ministry of Road Transport and Highways (MORTH) reported in year 2016 that the highest number of road accidents in India happened at intersections accounting for nearly thirty seven percent (37%) of the total crashes that took place. Even though traffic signals are considered to be the most effective way of controlling the traffic, more than 4300 lost their lives at signalized intersections in India. One of the main contributing factor in traffic signal related crashes is the presence of dilemma zone, where a driver becomes indecisive whether to pass or stop at the intersection on the yellow onset. Significant amount of research has been done on the dilemma driver behavior under homogeneous traffic conditions, however little or no research has been found on mixed traffic conditions, where vehicles do vary in physical and dynamic characteristics. The main motive of this study is to investigate the factors influencing the driver behavior in dilemma zone at signalized approaches in India under mixed traffic conditions. Field data was collected at five signalized approaches using video capturing technique to investigate the driver behavior. Frame by frame manual extraction resulted in 1054 driver responses at the yellow onset and binary logistic regression model is developed to represent the observed behavior. Distance from stop line, vehicle’s approach speed and type of intersection were found to be important factors in drivers stop/go decisions. Vehicle type, which is a characteristic of mixed traffic conditions is found to have a significant impact on the driver’s decision at the onset of yellow. The insights from this study findings can be used to enhance the safety and performance of signalized intersections in developing countries. Ó 2018 Elsevier Ltd. All rights reserved.
1. Introduction Intersections are vital node points in the road network, where two or more traffic streams cross or merge. The efficiency of the road network as a whole depends on the operational efficiency of these intersections. For safe maneuvering of the vehicular and pedestrian traffic at the intersections they are signalized with optimum signal time, where conflicts are addressed by time sharing principle. The timing of the yellow signal at signalized intersection is considered to be critical as the safety at the intersections is directly related with the timings of these clearance intervals, which is used as transition between the conflicting traffic movements. Even though traffic signals are considered to be the most effective way of controlling the traffic, they stand second in the fatal accidents next to the un-signalized intersections (MORTH, 2016). Among all ⇑ Corresponding author. E-mail addresses: [email protected] (B.K. Pathivada), [email protected] (V. Perumal). https://doi.org/10.1016/j.trf.2018.10.010 1369-8478/Ó 2018 Elsevier Ltd. All rights reserved.
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possible factors contributing to the traffic signal related crashes, complexity of driver’s decision-making process at the intersection dilemma zone is one of the major cause. As the signal changes from green to yellow, vehicles travelling at higher speeds to clear the intersection are often caught in a zone where they have to decide whether to proceed or stop at the intersection, which is termed as dilemma zone. There are two types of dilemma zone found in literature namely, type I and type II dilemma zone (Gates, Noyce, Laracuente, & Assistant, 2007; Urbanik & Koonce, 2007; Wei, Li, Yi, & Duemmel, 2011; Zhang, Fu, & Hu, 2014) as shown in Fig. 1. Type I dilemma zone is an area, where a driver can ‘‘neither” safely pass nor comfortably stop at the stop line approaching the intersection at the yellow onset (Gazis, Herman, & Maradudin, 1960). Type II dilemma zone was first recognized in a committee report produced by the southern section of Institute of Transportation Engineers (ITE) in 1974 (Zhang et al., 2014). It is an area where the driver is indecisive to stop or cross at the onset of yellow signal (Gates et al., 2007; Wei et al., 2011). Type I dilemma zone is attributed to the improper signal timing whereas, type II dilemma zone is attributed to the complications in driver decision making process (Urbanik & Koonce, 2007; Zhang et al., 2014). Understanding this dilemma driver behavior has a significant effect on eliminating the dilemma zone and increasing the safety at signalized intersections. This study focuses in identifying the factors influencing the driver’s decision-making behavior under mixed traffic conditions, such as effect of vehicle and intersection type at the onset of yellow. 2. Literature review Dilemma zone has been a research interest from early 1960s after it was first referred in literature (Gazis et al., 1960). Type I dilemma zone is considered to be deterministic and it could be eliminated by providing minimum yellow interval according to GHM (Gazi, Herman & Maraduin) model (Gazis et al., 1960). Eventually researchers found a second type of dilemma zone, which is stochastic due to the varying driver behavior. Type II dilemma zone is also called Indecision zone or Option Zone (Ghanipoor Machiani & Abbas, 2014). This study focuses on the second type of dilemma zone, to understand the complexities of driver’s decision-making process under mixed traffic conditions. 2.1. Factors influencing driver decision Various researchers have explored associated factors that influence the driver’s decision behavior at the onset of yellow. Higher approach speeds and shorter distance to the stop line decreased the probability of stopping at the intersection (Kim, Zhang, Fujiwara, Jang, & Namgung, 2008; Köll, Bader, & Axhausen, 2004; Pathivada & Perumal, 2017). Vehicles were more likely to stop at the intersection without the countdown time compared to the intersection with countdown timer (Long, Liu, & Han, 2013; Yang, Tian, Wang, Zhou, & Liang, 2014). Drivers were more likely to stop at the intersections with shorter yellow duration, longer cycle length and presence of pedestrians on the side street (Gates et al., 2007). Driver attributes such as gender and age groups were found to have an significant impact on the driver’s decision at the onset of yellow (Papaioannou, 2007; Rakha, Amer, & El-Shawarby, 2008) and males were found to be more aggressive compared to their female counterparts (Chang, Franz, & Yang, 2012). Flashing green ahead of the yellow signal tends to increase the number of early stops at the intersection (Köll et al., 2004). Posted speed limit and 85th percentile speed of the vehicles affected the likelihood of stopping behavior (Zhang et al., 2014). Type of vehicle had an significant effect on the red light running occurrences (Gates & Noyce, 2010). Acceleration of the vehicle and Time to Intersection (TTI) were found to be influencing parameters in the crossing behavior (Sharma, Bullock, & Peeta, 2011; Sheffi & Mahmassani, 1981).
Fig. 1. Type I and Type II Dilemma Zone.
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2.2. Sources of observed data To study the factors influencing the driver’s crossing behavior researchers have collected the driver observations from various sources. Field test bed was used where drivers were asked to drive the vehicle at given conditions to study their decision behavior (Amer, Rakha, & El-Shawarby, 2011; Rakha et al., 2008). Few other researchers used driving simulator to study the dilemma driver behavior (Abbas, Machiani, Garvey, Farkas, & Lord-Attivor, 2014; Moore & Hurwitz, 2013). 2.3. Modeling techniques Various modeling techniques were used to explain the dilemma driver behavior observed at the signalized intersections. Logistic regression model was the most commonly used model to explain the behavior (Gates & Noyce, 2010; Kim, et al., 2008; Köll et al., 2004; Papaioannou, 2007; Pathivada & Perumal, 2018). Few studies used probit model to describe the behavior (Sharma, et al., 2011; Sheffi & Mahmassani, 1981). Decision trees were also used to classify the stop/go decision behavior (Elmitiny, Yan, Radwan, Russo, & Nashar, 2010). Classical logic only permits conclusion of yes or no, so researchers used fuzzy logic model to explain the proportions of variable answers (Moore & Hurwitz, 2013; Yang et al., 2014). Agent based models were also found in literature explaining driver decision behavior (Abbas et al., 2014; Amer et al., 2011). 2.4. Research gap and motivation Literature indicates that the studies have addressed the issue of dilemma driver behavior for homogeneous traffic conditions, where cars and trucks are the predominant vehicle types. These study findings might not be directly applicable or transferable for mixed traffic conditions, which is characterized by the presence of diverse vehicle types varying in their physical characteristics such as shape, size etc., and dynamic characteristics such as acceleration, deceleration capabilities etc. Complexities in decision making process increases with presence of smaller vehicles such as motorized two wheelers creeping through the gaps available in the traffic. Dilemma zone issue becomes more prevalent with greater variability in vehicle operating speeds. The motive of this study is to understand the possible influencing factors that affect the driver’s decision-making process under mixed traffic conditions and model the dilemma driver behavior at the onset of yellow. 3. Methodology 3.1. Details of selected locations To study the dilemma driver behavior under mixed traffic conditions, data was collected at five signalized intersections having typical roadway and traffic conditions in the city of Mumbai, India. A typical intersection approach with different vehicle types plying on the Indian roads is shown in Fig. 2. Signalized Intersections selected were of typical 3-legged and 4-legged with fixed signal timings. Approaches at the intersections were selected in such a way that there was clear visibility up to at least 100 m from the stop line and feasibility of setting camera at a higher elevation. Selected approaches were relatively straight and flat to neglect any effects of grades and curves, free from pot holes, side encroachment and are access controlled so that there will be no obstruction to the vehicles approaching at their desired speeds. Details and characteristics of the selected intersection approaches are shown in Table 1. 3.2. Data collection Video capturing technique was used to collect the data at the selected signalized intersection approaches. Video data was collected during the non-peak hours (11 am to 3 pm), as dilemma behavior could not be observed during peak hours due to
Fig. 2. Typical Intersection approach.
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Table 1 Characteristics of selected signalized approaches. Intersections
Site 1
Site 2
Site 3
Site 4
Site 5
Intersection name Type of Intersection Subject Approach Cycle length Yellow duration Green phase duration Target approach green ratio No. of lanes in subject approach No. of recorded observations
Godrej 3-arm East 130 sec 5 sec 90 sec 0.69 5 291
Tagore 4-arm North 100 sec 3 sec 35 sec 0.35 5 208
Palm 3-arm South 115 sec 4 sec 35 sec 0.31 4 214
CST 4-arm South 199 sec 3 sec 120 sec 0.60 3 199
Shyam 3-arm West 115 sec 3 sec 72 sec 0.63 3 142
the queueing. To ensure quality of data, high definition cameras were mounted at certain height to provide a full view of necessary intersection characteristics, approaching vehicles, traffic signal indication, location of the vehicle with respect to the stop line and whether or not the vehicle stopped or passed through. Typical video camera installations are shown in Fig. 3. Traffic cones were placed at 10 m interval and illuminating tape was used to mark additional points at the edge of the road along the curb. With the help of traffic cones, transverse trap lines were superimposed on the video data using video editing software for extracting the required observations (shown in Fig. 4). Average traffic composition at the selected study locations were found to be as 50.7% car, 11.39% truck, 6.72% motorized three-wheeler and 31.20% motorized two-wheeler. As the selected intersections were located on outer arterial road, the mode share of car and motorized two wheelers were higher than the other modes. Due to the fewer samples of busses and light commercial vehicles, they were combined with trucks in the analysis. 3.3. Data extraction For extracting all the required data, collected videos were digitized into 30 frames per second using video editor software. As the signal turns from green to yellow, the video was paused, and the position of the subject vehicle was noted with an accuracy of 10 m and the speed of the vehicle was captured as the time taken to cross a trap length of 30 m (shown in Fig. 4). This speed is measured 100 m ahead of the intersection and is referred as approach speed of the subject vehicle. Once a vehicle has crossed the stop line and entered the intersection, the driver response was recorded as ‘go’ and if the vehicle stops at the intersection, it was recorded as ‘stop’. Fig. 5 shows the driver responses (stop or cross) for different vehicle types recorded at the selected intersection approaches. The responses of the driver are plotted for a given distance from the stop line at the onset of yellow to the vehicle approaching speed. It can be clearly observed from the figure that the decision of the drivers overlapping, which depicts presence of option zone created at the onset of yellow. Sample size of motorized two wheelers and cars were more than that of the other vehicle types, as their traffic composition were predominant at the selected study locations. The data extracted includes response of driver (Stop or Cross), distance to stop line in m, approach speed of subject vehicles in Km per hour, type of subject vehicle (Car, motorized two wheeler, truck and motorized three wheeler), signal characteristics and intersection geometry. All the extracted variables along with their description is shown in Table 2. 4. Data analysis and modeling Observations from all the five signalized intersection approaches were extracted manually with frame by frame analysis of the video data collected, which resulted in a total of 1054 driver responses at the onset of yellow. The observations
(a)
(b)
Fig. 3. Video Camera Installed at study Locations.
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Fig. 4. Field and Laboratory setup for capturing dilemma behavior.
recorded were only for through moving vehicles, which had a chance to make a stop/go decision at the onset of yellow. Vehicles which were forced to stop because of the following behavior and the turning vehicles which tend to slow down at the intersection were not considered as they do not exhibit dilemma behavior. There were fewer red-light running occurrences in the dataset as the observations recorded were only within 100 m from the stop line. Descriptive statistics of the extracted data is shown in Table 3. 4.1. Modeling driver decision behavior at signalized intersection crossing The objective of the model is to be able to predict the driver’s probability of stopping or passing at a given situation. Given that the driver has encountered a yellow signal while approaching the intersection, he has two choices either to pass or stop at the intersection. A binary logistic regression can better explain this behavior as a function of various explanatory variables. Choice made by the driver is statistically related to the attributes of various factors influencing their decision. The probability of a driver, i stopping at the intersection is given by:
Pi ðStopÞ
¼
1 1 þ expðU i Þ
ð1Þ
Utility function of an alternative can be expressed as:
U ji ¼ bo þ bj1 xj1 þ bj2 xj2 þ þ bjn xjn
ð2Þ
Where U ji = Utility of driver i choosing alternative j; j = Alternative (Stop or go); n = Number of independent variables; b = Model coefficients. Various possible combinations of continuous and discrete variables affecting the driver behavior at the yellow onset were tried and the final combination of the variables were decided based on p-value at 5% level of significance. Standard error of the estimates shown in Table 4 indicates the accuracy of predictions and p-value shows the significance of the variables at 95 percent confidence interval. Negative co-efficient for approaching vehicle speed indicates that the stopping probability decreases with the increase in approach speed and positive sign for the variable distance to stop line indicates that the stopping probability increases with the increase in vehicle’s distance from stop line. Type of intersection (3-arm or 4-arm)
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Fig. 5. Driver responses at yellow onset.
Table 2 Variables along with their description. Variable
Definition
Type
Approaching Vehicle Speed (VS) Distance to Stop line (DSL) Vehicle type (VT) Type of Intersection (TI) Duration of Yellow Interval (Y) Green time Ratio (GR) Red Light Running (RLR) Driver Response (DS)
Speed at which the vehicle approaches the intersection area, (Kmph) Position of the vehicle from the stop line at the onset of yellow signal, (m) 0 for Motorized two-wheeler; 1 for Car; 2 for Motorized three-wheeler; 3 for Truck/buses 0 for 3-arm; 1 for 4-arm intersection. Duration of the yellow signal provided at the selected intersection approach Ratio of green time of the target approach to cycle length 0 for non-red light running; 1 for red light running 0 for vehicle crossing at yellow interval;1 for vehicle stopping at yellow interval
Continuous Continuous Categorical Categorical Continuous Continuous Categorical Categorical
also had a significant impact on the driver’s stopping probability. Motorized two-wheeler was considered as the base vehicle in the model and other vehicle types have been incorporated in the model as dummy variables. 4.2. Model validation Validation for the developed model is carried out using the remaining 20 percent of driver responses recorded. For a given choice, if the predicted probability from the model is greater than 0.5, it is assumed that the model predicts the choice as stop
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B.K. Pathivada, V. Perumal / Transportation Research Part F 60 (2019) 111–120 Table 3 Descriptive statistics of the extracted data. Variable
Sub-level
N
Percentage (%)
Driver Response (DS)
Stop Go Yes No 3-arm 4-arm Car Truck MTW M3W
468 586 88 498 647 407 490 112 369 83
44.4 55.6 15.06 84.94 61.45 38.55 46.51 10.6 35.12 7.77
Red light Running (RLR) Intersection Type (TI) Vehicle Type (VT)
MTW: Motorized two-wheeler; M3W: Motorized three-wheeler.
Table 4 Statistics of developed model.
*
Variable
Description
Coefficient
Std. Error
t-stat
p-value
Constant DSL VS TI C M3W T McFadden’s R2
Constant Distance to stop line Approaching vehicle Speed Type of Intersection Car Motorized three-wheeler Truck
0.858 0.055 0.075 1.523 0.941 0.694 0.536 0.41
0.371 0.004 0.007 0.198 0.203 0.349 0.304
2.32 14.95 10.31 7.67 4.62 1.99 1.77
0.020 0.000 0.000 0.000 0.000 0.047 0.078*
Significant at 90 percent confidence interval.
and the model predicts that the driver will pass, if the predicted probability is less than 0.5. Prediction accuracy of the developed model is found out to be 82.3% (shown in Table 5). The proposed model with a McFadden pseudo R-squared value of 0.41 and prediction accuracy of 82.3% is strong enough to predict the driver behavior at the onset of yellow at signalized intersection. 5. Discussions 5.1. Effect of vehicle type on driver decision behavior The stopping probability curves at 40 km/h for different vehicle types have been shown in Fig. 6. It can be clearly observed that the stopping probability is greatly influenced by the vehicle type, one can clearly notice from the Fig. 6 that for a distance of say 60 m from stop line, the stopping probability of motorized two-wheeler, car, truck and motorized three-wheeler are 41%, 64%, 54% and 58% respectively. The stopping probability of the car is more than 1.5 times the stopping probability of the motorized two wheelers. It is logical because of the significant difference in the vehicle’s physical dimensions and dynamic characteristics influencing the maneuverability, so the driver tends to be more cautious based on his size of the vehicle and its acceleration/deceleration capabilities. 5.2. Effect of type of intersection on driver decision behavior behavior The stopping probability curves for a typical motorized two-wheeler at 40 Km/h for different intersection types have been shown in Fig. 7. One can clearly observe that the stopping probability at the 3-arm intersection is more that of the 4-arm intersection. It might be because of the cycle length at the 4-arm intersections are longer than that at the 3-arm intersections, and the drivers tend to be impatient and aggressive. As can be seen from the figure for a distance of say 50 m from the stop line, the stopping probability of a car at the 4-arm junction is 22% whereas it is 56% (2.5 times) at the 3-arm intersection. Table 5 Prediction success table for developed model. Observed choices
0 1 Column Total Correctly Predicted (%)
Predicted choices
Row total
0
1
84 22 106 84.84%
15 89 104 80.18%
Overall percentage
99 111 210 82.30
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Fig. 6. Stopping probability curves of different vehicle types.
Fig. 7. Stopping probability curves for type of intersection.
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6. Summary and conclusions In this study, data at five signalized intersection approaches were collected using video capturing technique for modeling the driver behavior at the onset of yellow. Given that the driver has two choices either to pass or stop at the intersection, a binary logistic regression model has been developed to explain this behavior. It was observed that the probability of stopping decreased with the increase in the approaching speed of the vehicles. Vehicles stopping probability decreased with the increase in their distance from the stop line. Vehicles were more likely to stop at a 3-arm signalized intersection than at a 4-arm signalized intersection. This might be because of the signal cycle length at the 4-arm intersections are longer than at the at the 3-arm intersections, and the drivers tent to be impatient and behave aggressively. Vehicle type, which is a characteristic of the mixed traffic conditions had significant effect on the stopping probability. Motorized two wheelers were less likely to stop and cars were more likely to stop at the intersection when compared to the other vehicle types. The developed model can be utilized to improve safety and operational performance of the signalized intersections in developing countries by providing a dynamic real time pre-signal or by developing an in-vehicle warning system to eliminate the option zone at the intersections. 7. Limitations and future scope The driver crossing behavior at the intersections are usually influenced by various internal and external factors such as intersection geometry, surrounding land-use, visibility of the signal, driver’s knowledge on the phases, type of vehicle, emotional state of the driver, distraction due to in-vehicle technology, and driver attributes etc. These factors can be considered in the future to enhance the driver decision model. It is difficult to generalize the developed model as number of other factors are influencing the driver’s decision. This study aimed at only investigating the influencing factors that could be observed from the field data obtained through video capturing technique. Also, the position of the vehicles was measured at an accuracy of 10 m, precise location of the vehicle from the stop line can enhance the model results. Efforts should be made to investigate other factors which cannot be observed from the field with the help of driving simulator or through a controlled experiment. Further, dilemma zone boundaries should be explored to establish various countermeasures to improve safety at the signalized intersections. Authors are currently working in these directions. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.trf.2018.10.010. References Abbas, M., Machiani, S. G., Garvey, P. M., Farkas, A., & Lord-Attivor, R. (2014). Modeling the Dynamics of Driver’s Dilemma Zone Perception Using Machine Learning Methods for Safer Intersection Control, 1–89. Retrieved from. Amer, A., Rakha, H., & El-Shawarby, I. (2011). Agent-based behavioral modeling framework of driver behavior at the onset of yellow indication at signalized intersections. In IEEE conference on intelligent transportation systems, proceedings (pp. 1809–1814). ITSC. Chang, G.-L., Franz, M. L., & Yang, L. (2012). Design and Evaluation of a Dynamic Dilemma Zone System for a High Speed Rural Intersection, (No. MD-12SP708B4J). Elmitiny, N., Yan, X., Radwan, E., Russo, C., & Nashar, D. (2010). Classification analysis of driver’s stop/go decision and red-light running violation. Accident Analysis & Prevention, 42(1), 101–111. https://doi.org/10.1016/j.aap.2009.07.007. Gates, T., & Noyce, D. (2010). Dilemma zone driver behavior as a function of vehicle type, time of day, and platooning. Transportation Research Record: Journal of the Transportation Research Board, 2149, 84–93. https://doi.org/10.3141/2149-10. Gates, T. J., Noyce, D. A., Laracuente, L., & Assistant, P. E. (2007). Analysis of dilemma zone driver behavior at signalized intersections Retrieved from. Transportation research record. Gazis, D., Herman, R., & Maradudin, A. (1960). The problem of the amber signal light in traffic flow. Operations Research, 8(1), 112–132. https://doi.org/ 10.1287/opre.8.1.112. Ghanipoor Machiani, S., & Abbas, M. (2014). Safety surrogate histograms (SSH): A novel real-time safety assessment of dilemma zone related conflicts at signalized intersections. Accident Analysis and Prevention. https://doi.org/10.1016/j.aap.2015.04.024. Kim, W., Zhang, J., Fujiwara, A., Jang, T. Y., & Namgung, M. (2008). Analysis of stopping behavior at urban signalized intersections: Empirical study in South Korea. Transportation Research Record: Journal of the Transportation Research Board. Köll, H., Bader, M., & Axhausen, K. W. (2004). Driver behaviour during flashing green before amber: A comparative study. Accident Analysis and Prevention, 36 (2), 273–280. https://doi.org/10.1016/S0001-4575(03)00005-8. Long, K., Liu, Y., & Han, L. D. (2013). Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China. Safety Science, 54, 8–16. https://doi.org/10.1016/j.ssci.2012.10.007. Moore, D., & Hurwitz, D. S. (2013). Fuzzy logic for improved dilemma zone identification. Transportation Research Record: Journal of the Transportation Research Board, 2384(1), 25–34. https://doi.org/10.3141/2384-04. MoRTH (2016). Road Accidents in India-2015. Transport Research Wing. New Delhi: Ministry of Road Transport & Highways. Papaioannou, P. (2007). Driver behaviour, dilemma zone and safety effects at urban signalised intersections in Greece. Accident Analysis and Prevention, 39 (1), 147–158. https://doi.org/10.1016/j.aap.2006.06.014. Pathivada, B. K., & Perumal, V. (2017). Modeling driver behavior in dilemma zone under mixed traffic conditions. In Transportation Research Procedia (pp. 961–968). Elsevier. Pathivada, B. K., & Perumal, V. (2018). Modeling driver behavior at signalized intersection dilemma zone under mixed traffic conditions Retrieved from. In Transportation Research Board (pp. 6). Rakha, H., Amer, A., & El-Shawarby, I. (2008). Modeling driver behavior within a signalized intersection approach decision-dilemma zone. Transportation Research Record: Journal of the Transportation Research Board, 2069(1), 16–25. https://doi.org/10.3141/2069-03.
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Sharma, A., Bullock, D., & Peeta, S. (2011). Estimating dilemma zone hazard function at high speed isolated intersection. Transportation Research Part C: Emerging Technologies, 19(3), 400–412. https://doi.org/10.1016/j.trc.2010.05.002. Sheffi, Y., & Mahmassani, H. (1981). A model of driver behavior at high speed signalized intersections. Transportation Science. https://doi.org/10.1287/ trsc.15.1.50. Urbanik, T., & Koonce, P. (2007). The dilemma with dilemma zone. Proceedings of ITE District, 6. Wei, H., Li, Z., Yi, P., & Duemmel, K. (2011). Quantifying dynamic factors contributing to dilemma zone at high-speed signalized intersections. Transportation Research Record: Journal of the Transportation Research Board, 2259(1), 202–212. https://doi.org/10.3141/2259-19. Yang, Z., Tian, X., Wang, W., Zhou, X., & Liang, H. (2014). Research on driver behavior in yellow interval at signalized intersections. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/518782. Zhang, Y., Fu, C., & Hu, L. (2014). Yellow light dilemma zone researches: A review. Journal of Traffic and Transportation Engineering (English Edition), 1(5), 338–352. https://doi.org/10.1016/S2095-7564(15)30280-4.
Contents lists available at ScienceDirect
Transportation Research Part F journal homepage: www.elsevier.com/locate/trf
Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions Bharat Kumar Pathivada ⇑, Vedagiri Perumal Department of Civil Engineering, Indian Institute of Technology (IIT) Bombay, Mumbai 400076, India
a r t i c l e
i n f o
Article history: Received 5 February 2018 Received in revised form 13 October 2018 Accepted 14 October 2018
Keywords: Driver behavior Dilemma zone Yellow signal Signalized Intersection Mixed traffic conditions Stop/Go decisions
a b s t r a c t Intersections are important node points in the road network, ensuring safe and efficient way of maneuvering the traffic. The Ministry of Road Transport and Highways (MORTH) reported in year 2016 that the highest number of road accidents in India happened at intersections accounting for nearly thirty seven percent (37%) of the total crashes that took place. Even though traffic signals are considered to be the most effective way of controlling the traffic, more than 4300 lost their lives at signalized intersections in India. One of the main contributing factor in traffic signal related crashes is the presence of dilemma zone, where a driver becomes indecisive whether to pass or stop at the intersection on the yellow onset. Significant amount of research has been done on the dilemma driver behavior under homogeneous traffic conditions, however little or no research has been found on mixed traffic conditions, where vehicles do vary in physical and dynamic characteristics. The main motive of this study is to investigate the factors influencing the driver behavior in dilemma zone at signalized approaches in India under mixed traffic conditions. Field data was collected at five signalized approaches using video capturing technique to investigate the driver behavior. Frame by frame manual extraction resulted in 1054 driver responses at the yellow onset and binary logistic regression model is developed to represent the observed behavior. Distance from stop line, vehicle’s approach speed and type of intersection were found to be important factors in drivers stop/go decisions. Vehicle type, which is a characteristic of mixed traffic conditions is found to have a significant impact on the driver’s decision at the onset of yellow. The insights from this study findings can be used to enhance the safety and performance of signalized intersections in developing countries. Ó 2018 Elsevier Ltd. All rights reserved.
1. Introduction Intersections are vital node points in the road network, where two or more traffic streams cross or merge. The efficiency of the road network as a whole depends on the operational efficiency of these intersections. For safe maneuvering of the vehicular and pedestrian traffic at the intersections they are signalized with optimum signal time, where conflicts are addressed by time sharing principle. The timing of the yellow signal at signalized intersection is considered to be critical as the safety at the intersections is directly related with the timings of these clearance intervals, which is used as transition between the conflicting traffic movements. Even though traffic signals are considered to be the most effective way of controlling the traffic, they stand second in the fatal accidents next to the un-signalized intersections (MORTH, 2016). Among all ⇑ Corresponding author. E-mail addresses: [email protected] (B.K. Pathivada), [email protected] (V. Perumal). https://doi.org/10.1016/j.trf.2018.10.010 1369-8478/Ó 2018 Elsevier Ltd. All rights reserved.
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possible factors contributing to the traffic signal related crashes, complexity of driver’s decision-making process at the intersection dilemma zone is one of the major cause. As the signal changes from green to yellow, vehicles travelling at higher speeds to clear the intersection are often caught in a zone where they have to decide whether to proceed or stop at the intersection, which is termed as dilemma zone. There are two types of dilemma zone found in literature namely, type I and type II dilemma zone (Gates, Noyce, Laracuente, & Assistant, 2007; Urbanik & Koonce, 2007; Wei, Li, Yi, & Duemmel, 2011; Zhang, Fu, & Hu, 2014) as shown in Fig. 1. Type I dilemma zone is an area, where a driver can ‘‘neither” safely pass nor comfortably stop at the stop line approaching the intersection at the yellow onset (Gazis, Herman, & Maradudin, 1960). Type II dilemma zone was first recognized in a committee report produced by the southern section of Institute of Transportation Engineers (ITE) in 1974 (Zhang et al., 2014). It is an area where the driver is indecisive to stop or cross at the onset of yellow signal (Gates et al., 2007; Wei et al., 2011). Type I dilemma zone is attributed to the improper signal timing whereas, type II dilemma zone is attributed to the complications in driver decision making process (Urbanik & Koonce, 2007; Zhang et al., 2014). Understanding this dilemma driver behavior has a significant effect on eliminating the dilemma zone and increasing the safety at signalized intersections. This study focuses in identifying the factors influencing the driver’s decision-making behavior under mixed traffic conditions, such as effect of vehicle and intersection type at the onset of yellow. 2. Literature review Dilemma zone has been a research interest from early 1960s after it was first referred in literature (Gazis et al., 1960). Type I dilemma zone is considered to be deterministic and it could be eliminated by providing minimum yellow interval according to GHM (Gazi, Herman & Maraduin) model (Gazis et al., 1960). Eventually researchers found a second type of dilemma zone, which is stochastic due to the varying driver behavior. Type II dilemma zone is also called Indecision zone or Option Zone (Ghanipoor Machiani & Abbas, 2014). This study focuses on the second type of dilemma zone, to understand the complexities of driver’s decision-making process under mixed traffic conditions. 2.1. Factors influencing driver decision Various researchers have explored associated factors that influence the driver’s decision behavior at the onset of yellow. Higher approach speeds and shorter distance to the stop line decreased the probability of stopping at the intersection (Kim, Zhang, Fujiwara, Jang, & Namgung, 2008; Köll, Bader, & Axhausen, 2004; Pathivada & Perumal, 2017). Vehicles were more likely to stop at the intersection without the countdown time compared to the intersection with countdown timer (Long, Liu, & Han, 2013; Yang, Tian, Wang, Zhou, & Liang, 2014). Drivers were more likely to stop at the intersections with shorter yellow duration, longer cycle length and presence of pedestrians on the side street (Gates et al., 2007). Driver attributes such as gender and age groups were found to have an significant impact on the driver’s decision at the onset of yellow (Papaioannou, 2007; Rakha, Amer, & El-Shawarby, 2008) and males were found to be more aggressive compared to their female counterparts (Chang, Franz, & Yang, 2012). Flashing green ahead of the yellow signal tends to increase the number of early stops at the intersection (Köll et al., 2004). Posted speed limit and 85th percentile speed of the vehicles affected the likelihood of stopping behavior (Zhang et al., 2014). Type of vehicle had an significant effect on the red light running occurrences (Gates & Noyce, 2010). Acceleration of the vehicle and Time to Intersection (TTI) were found to be influencing parameters in the crossing behavior (Sharma, Bullock, & Peeta, 2011; Sheffi & Mahmassani, 1981).
Fig. 1. Type I and Type II Dilemma Zone.
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2.2. Sources of observed data To study the factors influencing the driver’s crossing behavior researchers have collected the driver observations from various sources. Field test bed was used where drivers were asked to drive the vehicle at given conditions to study their decision behavior (Amer, Rakha, & El-Shawarby, 2011; Rakha et al., 2008). Few other researchers used driving simulator to study the dilemma driver behavior (Abbas, Machiani, Garvey, Farkas, & Lord-Attivor, 2014; Moore & Hurwitz, 2013). 2.3. Modeling techniques Various modeling techniques were used to explain the dilemma driver behavior observed at the signalized intersections. Logistic regression model was the most commonly used model to explain the behavior (Gates & Noyce, 2010; Kim, et al., 2008; Köll et al., 2004; Papaioannou, 2007; Pathivada & Perumal, 2018). Few studies used probit model to describe the behavior (Sharma, et al., 2011; Sheffi & Mahmassani, 1981). Decision trees were also used to classify the stop/go decision behavior (Elmitiny, Yan, Radwan, Russo, & Nashar, 2010). Classical logic only permits conclusion of yes or no, so researchers used fuzzy logic model to explain the proportions of variable answers (Moore & Hurwitz, 2013; Yang et al., 2014). Agent based models were also found in literature explaining driver decision behavior (Abbas et al., 2014; Amer et al., 2011). 2.4. Research gap and motivation Literature indicates that the studies have addressed the issue of dilemma driver behavior for homogeneous traffic conditions, where cars and trucks are the predominant vehicle types. These study findings might not be directly applicable or transferable for mixed traffic conditions, which is characterized by the presence of diverse vehicle types varying in their physical characteristics such as shape, size etc., and dynamic characteristics such as acceleration, deceleration capabilities etc. Complexities in decision making process increases with presence of smaller vehicles such as motorized two wheelers creeping through the gaps available in the traffic. Dilemma zone issue becomes more prevalent with greater variability in vehicle operating speeds. The motive of this study is to understand the possible influencing factors that affect the driver’s decision-making process under mixed traffic conditions and model the dilemma driver behavior at the onset of yellow. 3. Methodology 3.1. Details of selected locations To study the dilemma driver behavior under mixed traffic conditions, data was collected at five signalized intersections having typical roadway and traffic conditions in the city of Mumbai, India. A typical intersection approach with different vehicle types plying on the Indian roads is shown in Fig. 2. Signalized Intersections selected were of typical 3-legged and 4-legged with fixed signal timings. Approaches at the intersections were selected in such a way that there was clear visibility up to at least 100 m from the stop line and feasibility of setting camera at a higher elevation. Selected approaches were relatively straight and flat to neglect any effects of grades and curves, free from pot holes, side encroachment and are access controlled so that there will be no obstruction to the vehicles approaching at their desired speeds. Details and characteristics of the selected intersection approaches are shown in Table 1. 3.2. Data collection Video capturing technique was used to collect the data at the selected signalized intersection approaches. Video data was collected during the non-peak hours (11 am to 3 pm), as dilemma behavior could not be observed during peak hours due to
Fig. 2. Typical Intersection approach.
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Table 1 Characteristics of selected signalized approaches. Intersections
Site 1
Site 2
Site 3
Site 4
Site 5
Intersection name Type of Intersection Subject Approach Cycle length Yellow duration Green phase duration Target approach green ratio No. of lanes in subject approach No. of recorded observations
Godrej 3-arm East 130 sec 5 sec 90 sec 0.69 5 291
Tagore 4-arm North 100 sec 3 sec 35 sec 0.35 5 208
Palm 3-arm South 115 sec 4 sec 35 sec 0.31 4 214
CST 4-arm South 199 sec 3 sec 120 sec 0.60 3 199
Shyam 3-arm West 115 sec 3 sec 72 sec 0.63 3 142
the queueing. To ensure quality of data, high definition cameras were mounted at certain height to provide a full view of necessary intersection characteristics, approaching vehicles, traffic signal indication, location of the vehicle with respect to the stop line and whether or not the vehicle stopped or passed through. Typical video camera installations are shown in Fig. 3. Traffic cones were placed at 10 m interval and illuminating tape was used to mark additional points at the edge of the road along the curb. With the help of traffic cones, transverse trap lines were superimposed on the video data using video editing software for extracting the required observations (shown in Fig. 4). Average traffic composition at the selected study locations were found to be as 50.7% car, 11.39% truck, 6.72% motorized three-wheeler and 31.20% motorized two-wheeler. As the selected intersections were located on outer arterial road, the mode share of car and motorized two wheelers were higher than the other modes. Due to the fewer samples of busses and light commercial vehicles, they were combined with trucks in the analysis. 3.3. Data extraction For extracting all the required data, collected videos were digitized into 30 frames per second using video editor software. As the signal turns from green to yellow, the video was paused, and the position of the subject vehicle was noted with an accuracy of 10 m and the speed of the vehicle was captured as the time taken to cross a trap length of 30 m (shown in Fig. 4). This speed is measured 100 m ahead of the intersection and is referred as approach speed of the subject vehicle. Once a vehicle has crossed the stop line and entered the intersection, the driver response was recorded as ‘go’ and if the vehicle stops at the intersection, it was recorded as ‘stop’. Fig. 5 shows the driver responses (stop or cross) for different vehicle types recorded at the selected intersection approaches. The responses of the driver are plotted for a given distance from the stop line at the onset of yellow to the vehicle approaching speed. It can be clearly observed from the figure that the decision of the drivers overlapping, which depicts presence of option zone created at the onset of yellow. Sample size of motorized two wheelers and cars were more than that of the other vehicle types, as their traffic composition were predominant at the selected study locations. The data extracted includes response of driver (Stop or Cross), distance to stop line in m, approach speed of subject vehicles in Km per hour, type of subject vehicle (Car, motorized two wheeler, truck and motorized three wheeler), signal characteristics and intersection geometry. All the extracted variables along with their description is shown in Table 2. 4. Data analysis and modeling Observations from all the five signalized intersection approaches were extracted manually with frame by frame analysis of the video data collected, which resulted in a total of 1054 driver responses at the onset of yellow. The observations
(a)
(b)
Fig. 3. Video Camera Installed at study Locations.
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Fig. 4. Field and Laboratory setup for capturing dilemma behavior.
recorded were only for through moving vehicles, which had a chance to make a stop/go decision at the onset of yellow. Vehicles which were forced to stop because of the following behavior and the turning vehicles which tend to slow down at the intersection were not considered as they do not exhibit dilemma behavior. There were fewer red-light running occurrences in the dataset as the observations recorded were only within 100 m from the stop line. Descriptive statistics of the extracted data is shown in Table 3. 4.1. Modeling driver decision behavior at signalized intersection crossing The objective of the model is to be able to predict the driver’s probability of stopping or passing at a given situation. Given that the driver has encountered a yellow signal while approaching the intersection, he has two choices either to pass or stop at the intersection. A binary logistic regression can better explain this behavior as a function of various explanatory variables. Choice made by the driver is statistically related to the attributes of various factors influencing their decision. The probability of a driver, i stopping at the intersection is given by:
Pi ðStopÞ
¼
1 1 þ expðU i Þ
ð1Þ
Utility function of an alternative can be expressed as:
U ji ¼ bo þ bj1 xj1 þ bj2 xj2 þ þ bjn xjn
ð2Þ
Where U ji = Utility of driver i choosing alternative j; j = Alternative (Stop or go); n = Number of independent variables; b = Model coefficients. Various possible combinations of continuous and discrete variables affecting the driver behavior at the yellow onset were tried and the final combination of the variables were decided based on p-value at 5% level of significance. Standard error of the estimates shown in Table 4 indicates the accuracy of predictions and p-value shows the significance of the variables at 95 percent confidence interval. Negative co-efficient for approaching vehicle speed indicates that the stopping probability decreases with the increase in approach speed and positive sign for the variable distance to stop line indicates that the stopping probability increases with the increase in vehicle’s distance from stop line. Type of intersection (3-arm or 4-arm)
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Fig. 5. Driver responses at yellow onset.
Table 2 Variables along with their description. Variable
Definition
Type
Approaching Vehicle Speed (VS) Distance to Stop line (DSL) Vehicle type (VT) Type of Intersection (TI) Duration of Yellow Interval (Y) Green time Ratio (GR) Red Light Running (RLR) Driver Response (DS)
Speed at which the vehicle approaches the intersection area, (Kmph) Position of the vehicle from the stop line at the onset of yellow signal, (m) 0 for Motorized two-wheeler; 1 for Car; 2 for Motorized three-wheeler; 3 for Truck/buses 0 for 3-arm; 1 for 4-arm intersection. Duration of the yellow signal provided at the selected intersection approach Ratio of green time of the target approach to cycle length 0 for non-red light running; 1 for red light running 0 for vehicle crossing at yellow interval;1 for vehicle stopping at yellow interval
Continuous Continuous Categorical Categorical Continuous Continuous Categorical Categorical
also had a significant impact on the driver’s stopping probability. Motorized two-wheeler was considered as the base vehicle in the model and other vehicle types have been incorporated in the model as dummy variables. 4.2. Model validation Validation for the developed model is carried out using the remaining 20 percent of driver responses recorded. For a given choice, if the predicted probability from the model is greater than 0.5, it is assumed that the model predicts the choice as stop
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B.K. Pathivada, V. Perumal / Transportation Research Part F 60 (2019) 111–120 Table 3 Descriptive statistics of the extracted data. Variable
Sub-level
N
Percentage (%)
Driver Response (DS)
Stop Go Yes No 3-arm 4-arm Car Truck MTW M3W
468 586 88 498 647 407 490 112 369 83
44.4 55.6 15.06 84.94 61.45 38.55 46.51 10.6 35.12 7.77
Red light Running (RLR) Intersection Type (TI) Vehicle Type (VT)
MTW: Motorized two-wheeler; M3W: Motorized three-wheeler.
Table 4 Statistics of developed model.
*
Variable
Description
Coefficient
Std. Error
t-stat
p-value
Constant DSL VS TI C M3W T McFadden’s R2
Constant Distance to stop line Approaching vehicle Speed Type of Intersection Car Motorized three-wheeler Truck
0.858 0.055 0.075 1.523 0.941 0.694 0.536 0.41
0.371 0.004 0.007 0.198 0.203 0.349 0.304
2.32 14.95 10.31 7.67 4.62 1.99 1.77
0.020 0.000 0.000 0.000 0.000 0.047 0.078*
Significant at 90 percent confidence interval.
and the model predicts that the driver will pass, if the predicted probability is less than 0.5. Prediction accuracy of the developed model is found out to be 82.3% (shown in Table 5). The proposed model with a McFadden pseudo R-squared value of 0.41 and prediction accuracy of 82.3% is strong enough to predict the driver behavior at the onset of yellow at signalized intersection. 5. Discussions 5.1. Effect of vehicle type on driver decision behavior The stopping probability curves at 40 km/h for different vehicle types have been shown in Fig. 6. It can be clearly observed that the stopping probability is greatly influenced by the vehicle type, one can clearly notice from the Fig. 6 that for a distance of say 60 m from stop line, the stopping probability of motorized two-wheeler, car, truck and motorized three-wheeler are 41%, 64%, 54% and 58% respectively. The stopping probability of the car is more than 1.5 times the stopping probability of the motorized two wheelers. It is logical because of the significant difference in the vehicle’s physical dimensions and dynamic characteristics influencing the maneuverability, so the driver tends to be more cautious based on his size of the vehicle and its acceleration/deceleration capabilities. 5.2. Effect of type of intersection on driver decision behavior behavior The stopping probability curves for a typical motorized two-wheeler at 40 Km/h for different intersection types have been shown in Fig. 7. One can clearly observe that the stopping probability at the 3-arm intersection is more that of the 4-arm intersection. It might be because of the cycle length at the 4-arm intersections are longer than that at the 3-arm intersections, and the drivers tend to be impatient and aggressive. As can be seen from the figure for a distance of say 50 m from the stop line, the stopping probability of a car at the 4-arm junction is 22% whereas it is 56% (2.5 times) at the 3-arm intersection. Table 5 Prediction success table for developed model. Observed choices
0 1 Column Total Correctly Predicted (%)
Predicted choices
Row total
0
1
84 22 106 84.84%
15 89 104 80.18%
Overall percentage
99 111 210 82.30
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Fig. 6. Stopping probability curves of different vehicle types.
Fig. 7. Stopping probability curves for type of intersection.
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6. Summary and conclusions In this study, data at five signalized intersection approaches were collected using video capturing technique for modeling the driver behavior at the onset of yellow. Given that the driver has two choices either to pass or stop at the intersection, a binary logistic regression model has been developed to explain this behavior. It was observed that the probability of stopping decreased with the increase in the approaching speed of the vehicles. Vehicles stopping probability decreased with the increase in their distance from the stop line. Vehicles were more likely to stop at a 3-arm signalized intersection than at a 4-arm signalized intersection. This might be because of the signal cycle length at the 4-arm intersections are longer than at the at the 3-arm intersections, and the drivers tent to be impatient and behave aggressively. Vehicle type, which is a characteristic of the mixed traffic conditions had significant effect on the stopping probability. Motorized two wheelers were less likely to stop and cars were more likely to stop at the intersection when compared to the other vehicle types. The developed model can be utilized to improve safety and operational performance of the signalized intersections in developing countries by providing a dynamic real time pre-signal or by developing an in-vehicle warning system to eliminate the option zone at the intersections. 7. Limitations and future scope The driver crossing behavior at the intersections are usually influenced by various internal and external factors such as intersection geometry, surrounding land-use, visibility of the signal, driver’s knowledge on the phases, type of vehicle, emotional state of the driver, distraction due to in-vehicle technology, and driver attributes etc. These factors can be considered in the future to enhance the driver decision model. It is difficult to generalize the developed model as number of other factors are influencing the driver’s decision. This study aimed at only investigating the influencing factors that could be observed from the field data obtained through video capturing technique. Also, the position of the vehicles was measured at an accuracy of 10 m, precise location of the vehicle from the stop line can enhance the model results. Efforts should be made to investigate other factors which cannot be observed from the field with the help of driving simulator or through a controlled experiment. Further, dilemma zone boundaries should be explored to establish various countermeasures to improve safety at the signalized intersections. Authors are currently working in these directions. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.trf.2018.10.010. References Abbas, M., Machiani, S. G., Garvey, P. M., Farkas, A., & Lord-Attivor, R. (2014). Modeling the Dynamics of Driver’s Dilemma Zone Perception Using Machine Learning Methods for Safer Intersection Control, 1–89. Retrieved from
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