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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    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.
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    Citations

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    Cited by:

    1. 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).
    2. 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.
    3. 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).
    4. 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.

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