PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Driver performance through the yellow phase using video cameras at urban signalized intersections

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main objective of this research is to examine the influencing parameters of driver performance through the yellow phase at urban signalized intersections with and without red-light running (RLR) cameras. Data were collected to include the intersection type, vehicle type, turning movement type, whether the vehicle position is in a platoon or not, presence of RLR cameras, green light flash devices, pedestrians, and pavement markings. A total of 2168 driver observations were extracted. Only 33.3% of the drivers stopped before the stop line, 59% of the drivers passed the intersection through the yellow phase, and 7% of the drivers committed RLR violations. The results showed that drivers were more likely to stop before the stop line through the yellow phase at locations with RLR cameras, green light flash devices, pavement markings, where pedestrians were present, and at a four-leg intersection. Chi-square tests indicated that all parameters had a significant impact on driver performance, except for the type of turning movement.
Czasopismo
Rocznik
Strony
51--64
Opis fizyczny
Bibliogr. 33 poz.
Twórcy
  • Jordan University of Science & Technology (JUST) P.O. Box 3030 - Irbid 22110, Jordan
  • Yarmouk University (YU) P.O. Box 566 - Irbid 21163, Jordan
  • Jordan University of Science & Technology (JUST) P.O. Box 3030 - Irbid 22110, Jordan
  • Jordan University of Science & Technology (JUST) P.O. Box 3030 - Irbid 22110, Jordan
Bibliografia
  • 1. Naghawi, H. & Al Qatawneh, B. & Al Louzi, R. Evaluation of Automated Enforcement Program in Amman. Periodica Polytechnica Transportation Engineering. 2018. Vol. 46(4). P. 201-206.
  • 2. Zhang, Y. & Yan, X. & Li, X. & Wu, J. & Dixit, V.V. Red-light-running crashes’ classification, comparison, and risk analysis based on General Estimates System (GES) crash database. International journal of environmental research and public health. 2018. Vol. 15(6). No. 1290. P. 1-15.
  • 3. Gazis, D. & Herman, R. & Maradudin, A. The problem of the amber signal light in traffic flow. Operations Research. 1960. Vol. 8(1). P. 112-132.
  • 4. ITE, Institute of Transportation Engineers. Making Intersections Safer: A Toolbox of Engineering Countermeasures to Reduce. Publication No. IR-115. Department of Transportation. Federal Highway Administration (FHWA), United States. 2003.
  • 5. Koonce, P. & Rodegerdts, L. Traffic signal timing manual (No. FHWA-HOP-08-024). Department of Transportation, Federal Highway Administration (FHWA), United States. 2008.
  • 6. Abbas, M. & Machiani, S.G. & Garvey, P.M. & Farkas, A. & Lord-Attivor, R. Modeling the dynamics of driver’s dilemma zone perception using machine learning methods for safer intersection control. Mid-Atlantic Universities Transportation Center. 2014. No. MAUTC-2012-04.
  • 7. Yang, Z. & Tian, X. & Wang, W. & Zhou, X. & Liang, H., Research on driver behavior in yellow interval at signalized intersections. Mathematical Problems in Engineering. 2014. Vol. 2014. Article ID 518782. 8 p.
  • 8. Papaioannou, P. Driver behaviour, dilemma zone and safety effects at urban signalized intersections in Greece. Accident Analysis & Prevention. 2007. Vol. 39(1). P. 147-158.
  • 9. WHO, World Health Organization. Global Status Report on Road Safety 2015. Available at: https://www.who.int/violence_injury_prevention/road_safety_status/2015/en/#:~:text=The%20Gl obal%20status%20report%20on%20road%20safety%202015%2C%20reflecting%20information, rates%20in%20low%2Dincome%20countries.
  • 10. NHTSA, National Highway Traffic Safety Administration. Traffic Safety Facts - 2016. Washington, DC20590. 2018.
  • 11. FHWA, Federal Highway Administration. 2018. Intersection Safety. Available at: https://safety.fhwa.dot.gov/intersection.
  • 12. FHWA, Federal Highway Administration. 2007. Stop Red Light Running Facts and Statistics. U.S. Department of Transportation. FHWA Safety 2006. Available at: http://safety.fhwa.dot.gov/intersections/redl_facts.htm.
  • 13. JTI, Jordan Traffic Institute. Traffic Accidents in Jordan, 2017. Amman, Jordan. Available at: https://www.psd.gov.jo/images/traffic/traffic2017.pdf.
  • 14. DOS, Department of Statistics, Jordan. 2020. Available at: http://dosweb.dos.gov.jo/ar/.
  • 15. Liu, Y. & Chang, G.L. & Tao, R. & Hicks, T. & Tabacek, E. Empirical observations of dynamic dilemma zones at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board. 2007. Vol. 2035. P. 122-133.
  • 16. Alex, S. & Isaac, K.P. & Varghese, V. Modelling Driver Behaviour at Signalized Intersection in Indian Roads 2. Transportation Research Board (TRB) Annual Meeting. Paper No. 13-0257. Washington D.C. United States, 2013.
  • 17. Rakha, H. & El-Shawarby, I. & Setti, J.R. Characterizing driver behavior on signalized intersection approaches at the onset of a yellow-phase trigger. IEEE Transactions on Intelligent Transportation Systems. 2007. Vol. 8(4). P. 630-640.
  • 18. Lum, K.M. & Wong, Y.D. A before-and-after study of driver stopping propensity at red light camera intersections. Accident Analysis & Prevention. 2003. Vol. 35(1). P. 111-120.
  • 19. Köll, H. & Bader, M. & Axhausen, K.W., Driver behaviour during flashing green before amber: a comparative study. Accident Analysis & Prevention. 2004. Vol. 36(2). P. 273-280.
  • 20. 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. Vol. 13(12). P. 1213.
  • 21. Tang, K. & Xu, Y. & Wang, F. & Oguchi, T. Exploring stop-go decision zones at rural high- speed intersections with flashing green signal and insufficient yellow time in China. Accident Analysis & Prevention. 2016. Vol. 95. P. 470-478.
  • 22. Savolainen, P.T. & Sharma, A. & Gates, T.J. 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. Vol. 96. P. 351-360.
  • 23. Bao, J. & Chen, Q. & Luo, D. & Wu, Y. & Liang, Z. Exploring the impact of signal types and adjacent vehicles on drivers’ choices after the onset of yellow. Physica A: Statistical Mechanics and its Applications. 2018. Vol. 500. P. 222-236.
  • 24. Palat, B. & Delhomme, P. What factors can predict why drivers go through yellow traffic lights? An approach based on an extended theory of planned behavior. Safety Science. 2012. Vol. 50(3). P. 408-417.
  • 25. Haque, M.M. & Ohlhauser, A.D. & Washington, S. & Boyle, L.N. Decisions and actions of distracted drivers at the onset of yellow lights. Accident Analysis & Prevention. 2016. Vol. 96. P. 290-299.
  • 26. Lavrenz, S.M. & Pyrialakou, V.D. & Gkritza, K. Modeling driver behavior in dilemma zones: A discrete/continuous formulation with selectivity bias corrections. Analytic Methods in Accident Research. 2014. Vol. 3. P. 44-55.
  • 27. Xiong, H. & Narayanaswamy, P. & Bao, S. & Flannagan, C. & Sayer, J. How do drivers behave during indecision zone maneuvers? Accident Analysis & Prevention. 2016. Vol. 96. P. 274-279.
  • 28. Zhang, Y. & Fu, C. & Hu, L. Yellow light dilemma zone researches: a review. Journal of traffic and transportation engineering (English edition). 2014. Vol. 1(5). P. 338-352.
  • 29. 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. Article ID 1250827.
  • 30. Kim, W. & Zhang, J. & Fujiwara, A. & Jang, T. & Namgung, M. Analysis of stopping behavior at urban signalized intersections: empirical study in South Korea. Transportation Research Record: Journal of the Transportation Research Board. 2008. Vol. 2080. P. 84-91.
  • 31. Gates, T. & Savolainen, P. & Maria, H.U. Impacts of automated red-light running enforcement cameras on driver behavior. Transportation Research Board (TRB) Annual Meeting. 2014. Paper No. 14-0943. Washington D.C. United States.
  • 32. Gates, T.J. & Noyce, D.A. Dilemma zone driver behavior as a function of vehicle type, time of day, and platooning. Transportation Research Record. 2010. Vol. 2149(1). P. 84-93.
  • 33. 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 (TRB) 94th Annual Meeting. 2015. Paper No. 15-3723. Washington D.C. United States.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e7066b79-0aed-4258-b6e7-63f4a0cd7f94
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.