IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v73y2015icp39-52.html
   My bibliography  Save this article

Analysis of yellow-light running at signalized intersections using high-resolution traffic data

Author

Listed:
  • Lu, Guangquan
  • Wang, Yunpeng
  • Wu, Xinkai
  • Liu, Henry X.

Abstract

Many accidents occurring at signalized intersections are closely related to drivers’ decisions of running through intersections during yellow light, i.e., yellow-light running (YLR). Therefore it is important to understand the relationships between YLR and the factors which contribute to drivers’ decision of YLR. This requires collecting a large amount of YLR cases. However, existing data collection method, which mainly relies on video cameras, has difficulties to collect a large amount of YLR data. In this research, we propose a method to study drivers’ YLR behaviors using high-resolution event-based data from signal control systems. We used 8months’ high-resolution data collected by two stop-bar detectors at a signalized intersection located in Minnesota and identified over 30,000 YLR cases. To identify the possible reasons for drivers’ decision of YLR, this research further categorized the YLR cases into four types: “in should-go zone”, “in should-stop zone”, “in dilemma zone”, and “in optional zone” according to the driver’s location when signal turns to yellow. Statistical analysis indicates that the mean values of approaching speed and acceleration rate are significantly different for different types of YLR. We also show that there were about 10% of YLR drivers who cannot run through intersection before traffic light turns to red. Furthermore, based on a strong correlation between hourly traffic volume and number of YLR events, this research developed a regression model that can be used to predict the number of YLR events based on hourly flow rate. This research also showed that snowing weather conditions cause more YLR events.

Suggested Citation

  • Lu, Guangquan & Wang, Yunpeng & Wu, Xinkai & Liu, Henry X., 2015. "Analysis of yellow-light running at signalized intersections using high-resolution traffic data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 73(C), pages 39-52.
    • Handle: RePEc:eee:transa:v:73:y:2015:i:c:p:39-52
    DOI: 10.1016/j.tra.2015.01.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856415000026
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2015.01.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Paul L. Olson & Richard W. Rothery, 1961. "Driver Response to the Amber Phase of Traffic Signals," Operations Research, INFORMS, vol. 9(5), pages 650-663, October.
    2. Denos Gazis & Robert Herman & Alexei Maradudin, 1960. "The Problem of the Amber Signal Light in Traffic Flow," Operations Research, INFORMS, vol. 8(1), pages 112-132, February.
    3. Yosef Sheffi & Hani Mahmassani, 1981. "A Model of Driver Behavior at High Speed Signalized Intersections," Transportation Science, INFORMS, vol. 15(1), pages 50-61, February.
    4. Liu, Chiu & Herman, Robert & Gazis, Denos C., 1996. "A review of the yellow interval dilemma," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 333-348, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Baratian-Ghorghi, Fatemeh & Zhou, Huaguo & Zech, Wesley C., 2016. "Red-light running traffic violations: A novel time-based method for determining a fine structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 93(C), pages 55-65.
    2. Niu, Zhipeng & Hu, Xiaowei & Fatmi, Mahmudur & Qi, Shouming & Wang, Siqing & Yang, Haihua & An, Shi, 2023. "Parking occupancy prediction under COVID-19 anti-pandemic policies: A model based on a policy-aware temporal convolutional network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    3. Juan Li & Xudong Jia & Chunfu Shao, 2016. "Predicting Driver Behavior during the Yellow Interval Using Video Surveillance," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
    4. Du, Mengxiao & Liu, Jiahui & Chen, Qun, 2021. "Improving traffic efficiency during yellow lights using connected vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moon, Young J. & Coleman, Fred, 2003. "Dynamic dilemma zone based on driver behavior and car-following model at highway-rail intersections," Transportation Research Part B: Methodological, Elsevier, vol. 37(4), pages 323-344, May.
    2. Juan Li & Xudong Jia & Chunfu Shao, 2016. "Predicting Driver Behavior during the Yellow Interval Using Video Surveillance," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
    3. Bao, Ji & Chen, Qun & Luo, Dandan & Wu, Yuli & Liang, Zuli, 2018. "Exploring the impact of signal types and adjacent vehicles on drivers’ choices after the onset of yellow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 222-236.
    4. Zhang, Liping & Zhou, Kun & Zhang, Wei-bin & Misener, James A., 2011. "Dynamic All-Red Extension at Signalized Intersection: Probabilistic Modeling and Algorithm," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7kp0030b, Institute of Transportation Studies, UC Berkeley.
    5. Cassidy, Michael & Coifman, Benjamin, 1998. "Design Of A Machine Vision-based, Vehicle Actuated Traffic Signal Controller," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2rg0957h, Institute of Transportation Studies, UC Berkeley.
    6. Tomasz Krukowicz & Krzysztof Firląg & Józef Suda & Mirosław Czerliński, 2021. "Analysis of the Impact of Countdown Signal Timers on Driving Behavior and Road Safety," Energies, MDPI, vol. 14(21), pages 1-33, October.
    7. Ventevogel, W.J., 1978. "On the configuration of a one-dimensional system of interacting particles with minimum potential energy per particle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 92(3), pages 343-361.
    8. Bara’ W. AL-MISTAREHI & Ahmad H. ALOMARI & Mohammed T. OBAIDAT & Areen A. AL-JAMMAL, 2021. "Driver Performance Through The Yellow Phase Using Video Cameras At Urban Signalized Intersections," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 16(1), pages 51-64, March.
    9. Liu, Chiu & Herman, Robert & Gazis, Denos C., 1996. "A review of the yellow interval dilemma," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 333-348, September.
    10. Grimm, Markus & Wagner, Max, 1994. "Mode densities of defect lines in three-dimensional Montroll-Potts lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 210(1), pages 1-23.
    11. Hossain, M., 2001. "Estimation of saturation flow at signalised intersections of developing cities: a micro-simulation modelling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(2), pages 123-141, February.
    12. Denos C. Gazis, 2002. "The Origins of Traffic Theory," Operations Research, INFORMS, vol. 50(1), pages 69-77, February.
    13. Wagner, Max & Mougios, Theodor, 1990. "Thermodynamics of mesoscopic soft modes in strongly disturbed lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 166(2), pages 229-262.
    14. Juan Li & Qinglian He & Hang Zhou & Yunlin Guan & Wei Dai, 2016. "Modeling Driver Behavior near Intersections in Hidden Markov Model," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
    15. Felderhof, B.U., 1985. "Wigner solids and diffusion controlled reactions in a regular array of spheres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 130(1), pages 34-56.
    16. Venema, P. & Bedeaux, D., 1989. "Reaction-diffusion on a periodic array of penetrable spherical sinks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 156(3), pages 835-852.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:73:y:2015:i:c:p:39-52. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.