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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
20.12.2023
DOI
https://doi.org/10.7307/ptt.v35i6.287
ISSUE
2023 (Vol 35), Issue 6
LICENSE
Copyright (c) 2024 Ahmad H. ALOMARI, Bara’ W. AL-MISTAREHI, Al-Jammal A. AL-JAMMAL, Taqwa I. ALHADIDI, Motasem S. OBEIDAT
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

Authors:Ahmad H. ALOMARI, Bara’ W. AL-MISTAREHI, Al-Jammal A. AL-JAMMAL, Taqwa I. ALHADIDI, Motasem S. OBEIDAT

Abstract

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate.

Keywords:traffic signal, traffic safety, logistic regression, machine learning, yellow phase, red light running

References

  1. [1] Hussain Q, et al. Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study. Accident Analysis & Prevention. 2020;134:105349. DOI: 10.1016/j.aap.2019.105349.
  2. [2] Elmitiny N, et al. Classification analysis of driver's stop/go decision and red-light running violation. Accident Analysis & Prevention. 2010;42(1):101-111. DOI: 10.1016/j.aap.2009.07.007.
  3. [3] Papaioannou P. Driver behaviour, dilemma zone and safety effects at urban signalised intersections in Greece. Accident Analysis & Prevention. 2007;39(1):147-158.‏ DOI: 10.1016/j.aap.2006.06.014.
  4. [4] Li Z, Wei H. Modeling dynamics of dilemma zones by formulating dynamical contributing factors with video-observed trajectory data. Procedia-Social and Behavioral Sciences. 2013;80:880-900. DOI: 10.1016/j.sbspro.2013.05.048.
  5. [5] Liu Y, et al. Empirical observations of dynamic dilemma zones at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board. 2007;2035(1):122-133. DOI: 10.3141/2035-14.
  6. [6] Gates T, Savolainen P, Maria HU. Impacts of automated red-light running enforcement cameras on driver behavior. Transportation Research Board (TRB) Annual Meeting, Washington D.C., United States, 2014. Paper No. 14-0943. 2014.
  7. [7] Savolainen PT, Sharma A, Gates TJ. Driver decision-making in the dilemma zone – Examining the influences of clearance intervals, enforcement cameras and the provision of advance warning through a panel data random parameters probit model. Accident Analysis & Prevention. 2016;96:351-360. DOI: 10.1016/j.aap.2015.08.020.
  8. [8] Awad W, et al. Drivers’ behavior at signalized intersections. Proceedings of the Seventh Traffic Safety Conference, 12-13 May, 2015, Amman, Jordan. 2015.
  9. [9] Zhang Y, Fu C, Hu L. Yellow light dilemma zone research: A review. Journal of Traffic and Transportation Engineering. 2014;1(5):338-352. DOI: 10.1016/S2095-7564(15)30280-4.
  10. [10] Rakha H, Amer A, El-Shawarby I. Modeling driver behavior within a signalized intersection approach decision-dilemma zone. Transportation Research Record: Journal of the Transportation Research Board. 2008;(2069):16-25. DOI: 10.3141/2069-03.
  11. [11] Pathivada BK, Perumal V. Modeling driver behavior in dilemma zone under mixed traffic conditions. Transportation Research Procedia. 2017;27:961-968. DOI: 10.1016/j.trpro.2017.12.120.
  12. [12] Pathivada BK, Perumal V. Analyzing dilemma driver behavior at signalized intersection under mixed traffic conditions. Transportation Research Part F: Traffic Psychology and Behaviour. 2019;60:111-120. DOI: 10.1016/j.trf.2018.10.010.
  13. [13] Li J, Jia X, Shao C. Predicting driver behavior during the yellow interval using video surveillance. International JOUrnal of Environmental Research and Public Health. 2016;13(12):1213. DOI: 10.3390/ijerph13121213.
  14. [14] Alex S, Isaac KP, Varghese V. Modelling driver behavior at signalized intersection in Indian roads. Transportation Research Board (TRB) Annual Meeting, Washington D.C., United States, 2013. Paper No. 13-0257. 2013.
  15. [15] Dong S, Zhou J. A comparative study on drivers’ stop/go behavior at signalized intersections based on decision tree classification model. Journal of Advanced Transportation. 2020;2020(2):1-13. DOI: 10.1155/2020/1250827.
  16. [16] Wang F, et al. Modeling risky driver behavior under the influence of flashing green signal with vehicle trajectory data. Transportation Research Record. 2016;2562(1):53-62. DOI: 10.3141/2562-07.
  17. [17] Gates TJ, Noyce DA, Laracuente L, Nordheim EV. Analysis of driver behavior in dilemma zones at signalized intersections. Transportation Research Record. 2007;2030(1):29-39. DOI: 10.3141/2030-05.
  18. [18] El-Shawarby I, Abdel-Salam ASG, Rakha H. Evaluation of driver perception–reaction time under rainy or wet roadway conditions at onset of yellow indication. Transportation Research Record: Journal of the Transportation Research Board. 2013;2384(1):18-24. DOI: 10.3141/2384-03.
  19. [19] Campisi T, et al. Comparison of red-light running (RLR) and yellow light running (YLR) traffic violations in the cities of Enna and Thessaloniki. Transportation Research Procedia. 2020;45:947-954. DOI: 10.1016/j.trpro.2020.02.072.
  20. [20] Ingale A, et al. Understanding driver behavior at intersection for mixed traffic conditions using questionnaire survey. Transportation Research. 2020:647-661. DOI: 10.1007/978-981-32-9042-6_51.
  21. [21] Swake J, Jannat M, Islam M, Hurwitz D. Driver response to phase termination at signalized intersections: Are driving simulator results valid. Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design: Driving Assessment 2013, 17-20 June 2013, Bolton Landing, New York, USA. 2013. p. 278-284. DOI: 10.17077/drivingassessment.1501.
  22. [22] Choudhary P, Velaga NR. Driver behaviour at the onset of yellow signal: A comparative study of distraction caused by use of a phone and a music player. Transportation Research Part F: Traffic Psychology & Behaviour. 2019;62:135-148. DOI: 10.1016/j.trf.2018.12.022.
  23. [23] Bryant CW, Rakha HA, El-Shawarby I. Study of truck driver behavior for design of traffic signal yellow and clearance timings. Transportation Research Record. 2015;2488(1):62-70. DOI: 10.3141/2488-07.
  24. [24] Banerjee S, Jeihani M, Khadem NK, Kabir MM. Influence of red-light violation warning systems on driver behavior – A driving simulator study. Traffic Injury Prevention. 2020;21(4):265-271. DOI: 10.1080/15389588.2020.1744135.
  25. [25] Najmi A, Choupani AA, Aghayan I. Characterizing driver behavior in dilemma zones at signalized roundabouts. Transportation research part F: Traffic Psychology and Behaviour. 2019;63:204-215. DOI: 10.1016/j.trf.2019.04.007.
  26. [26] Sun J, Wang Z, Yang J, Ouyang J. Comparison of dilemma zone and driver behavior of trucks and passenger cars at high-speed signalized intersections. Transportation Research Board 94th Annual Meeting, 2015, Washington DC, United States. 2015.
  27. [27] Ni Y, Wang M, Li K, Xue N. Impacts of Chinese’s new regulation of yellow signal on driving behavior and rear-end collision potential. Transportation Research Board 93rd Annual Meeting, 2014, Washington DC, United States. 2014.
  28. [28] Biswas S, Ghosh I. Modeling of the drivers’ decision-making behavior during yellow phase. KSCE Journal of Civil Engineering. 2018;22:4602-4614. DOI: 10.1007/s12205-018-0666-6.
  29. [29] Hurwitz DS, et al. Fuzzy sets to describe driver behavior in the dilemma zone of high-speed signalized intersections. Transportation Research Part F: Traffic Psych. & Behavior. 2012;15(2):132-143. DOI: 10.1016/j.trf.2011.11.003.
  30. [30] Yang Z, et al. Research on driver behavior in yellow interval at signalized intersections. Mathematical Problems in Engineering. 2014;2014:518782. DOI: 10.1155/2014/518782.
  31. [31] Tang K, Zhu S, Xu Y, Wang F. Modeling drivers' dynamic decision-making behavior during the phase transition period: An analytical approach based on hidden markov model theory. IEEE Transactions on Intelligent Transportation Systems. 2016;17(1):206-214. DOI: 10.1109/TITS.2015.2462738.
  32. [32] Elhenawy M, Rakha HA, El-Shawarby I. Enhanced modeling of driver stop-or-run actions at a yellow indication: Use of historical behavior and machine learning methods. Transportation Research Record. 2014;2423(1):24-34. DOI: 10.3141/2423-04.
  33. [33] Elhenawy M, Jahangiri A, Rakha HA, El-Shawarby I. Classification of driver stop/run behavior at the onset of a yellow indication for different vehicles and roadway surface conditions using historical behavior. Procedia Manufacturing. 2015;3:858-865. DOI: 10.1016/j.promfg.2015.07.342.
  34. [34] Elhenawy M, Jahangiri A, Rakha HA, El-Shawarby I. Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions. Accident Analysis & Prevention. 2015;83:90-100. DOI: 10.1016/j.aap.2015.06.016.
  35. [35] Khanfar NO, et al. Driving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning. International Journal of Injury Control and Safety Promotion. 2022:30(3):1-11. DOI: 10.1080/17457300.2022.2103573.
  36. [36] Jahangiri A, Rakha H, Dingus TA. Predicting red-light running violations at signalized intersections using machine learning techniques. Transportation Research Board 94th Annual Meeting, 2015, Washington DC, United States. 2015.
  37. [37] Tawfeek MH. Perceptual-based driver behaviour modelling at the yellow onset of signalised intersections. Journal of Transportation Safety & Security. 2022;14(3):404-429. DOI: 10.1080/19439962.2020.1783414.
  38. [38] Karri SL, et al. Identification and classification of driving behaviour at signalized intersections using support vector machine. International Journal of Automation and Computing. 2021;18:480-491. DOI: 10.1007/s11633-021-1295-y.
  39. [39] Karri SL, et al. Classification and prediction of driving behaviour at a traffic intersection using SVM and KNN. SN Computer Science. 2021;2:1-11. DOI: 10.1007/s42979-021-00588-7.
  40. [40] Alomari AH, Al-Mistarehi BW, Alnaasan TK, Obeidat MS. Utilizing different machine learning techniques to examine speeding violations. Appl. Sci. 2023;13:5113. DOI: 10.3390/app13085113.
  41. [41] Al-Mistarehi BW, Alomari AH, Obaidat MT, Al-Jammal AA. Driver performance through the yellow phase using video cameras at urban signalized intersections. Transport Problems. 2021:16(1):51-64. DOI: 10.21307/tp-2021-005.
  42. [42] Long K, Liu Y, Han LD. Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China. Safety Science. 2013;54:8-16. DOI: 10.1016/j.ssci.2012.10.007.
  43. [43] Zhang S, et al. Learning k for KNN classification. ACM Transactions on Intelligent Systems and Technology (TIST). 2017;8(3):1-19. DOI: 10.1145/2990508.
  44. [44] Wu X, et al. Top 10 algorithms in data mining. Knowledge and Information Systems. 2008;14:1-37. DOI: 10.1007/s10115-007-0114-2.
  45. [45] Friedman JH, Baskett F, Shustek LJ. An algorithm for finding nearest neighbors. IEEE Transactions on Computers. 1975;100(10):1000-6.
  46. [46] Hsu CW, Lin CJ. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks. 2002;13(2):415-25. DOI: 10.1109/72.991427.
  47. [47] Breiman, L. Random forests. Machine Learning. 2001;45:5-32. DOI: 10.1023/A:1010933404324.
  48. [48] Freund Y, Shapire R. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence. 1999;14(5):771-780.
  49. [49] Friedman J, Hastie T, Tibshirani R. Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics. 2000;28(2):337-407. DOI: 10.1214/aos/1016218223.
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