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Cycle-by-cycle intersection queue length distribution estimation using sample travel times

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Listed:
  • Hao, Peng
  • Ban, Xuegang (Jeff)
  • Guo, Dong
  • Ji, Qiang

Abstract

We propose Bayesian Network based methods for estimating the cycle by cycle queue length distribution of a signalized intersection. Queue length here is defined as the number of vehicles in a cycle which have experienced significant delays. The data input to the methods are sample travel times from mobile traffic sensors collected between an upstream location and a downstream location of the intersection. The proposed methods first classify traffic conditions and sample scenarios to seven cases. BN models are then developed for each case. The methods are tested using data from NGSIM, a field experiment, and microscopic traffic simulation. The results are satisfactory compared with two specific queue length estimation methods previously developed in the literature.

Suggested Citation

  • Hao, Peng & Ban, Xuegang (Jeff) & Guo, Dong & Ji, Qiang, 2014. "Cycle-by-cycle intersection queue length distribution estimation using sample travel times," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 185-204.
    • Handle: RePEc:eee:transb:v:68:y:2014:i:c:p:185-204
    DOI: 10.1016/j.trb.2014.06.004
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    References listed on IDEAS

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    1. Comert, Gurcan & Cetin, Mecit, 2009. "Queue length estimation from probe vehicle location and the impacts of sample size," European Journal of Operational Research, Elsevier, vol. 197(1), pages 196-202, August.
    2. Hofleitner, Aude & Herring, Ryan & Bayen, Alexandre, 2012. "Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1097-1122.
    3. Sun, Zhanbo & Zan, Bin & Ban, Xuegang (Jeff) & Gruteser, Marco, 2013. "Privacy protection method for fine-grained urban traffic modeling using mobile sensors," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 50-69.
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    Citations

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    Cited by:

    1. Comert, Gurcan, 2016. "Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters," European Journal of Operational Research, Elsevier, vol. 252(2), pages 502-521.
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    3. Wong, Wai & Shen, Shengyin & Zhao, Yan & Liu, Henry X., 2019. "On the estimation of connected vehicle penetration rate based on single-source connected vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 169-191.
    4. Wang, Zhengli & Zhu, Liyun & Ran, Bin & Jiang, Hai, 2020. "Queue profile estimation at a signalized intersection by exploiting the spatiotemporal propagation of shockwaves," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 59-71.
    5. Hao, Peng & Ban, Xuegang, 2015. "Long queue estimation for signalized intersections using mobile data," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 54-73.
    6. Yang, Qiaoli & Shi, Zhongke, 2018. "The evolution process of queues at signalized intersections under batch arrivals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 413-425.
    7. Farrell, Jay A & Wu, Guoyuan & Hu, Wang & Oswald, David & Hao, Peng, 2023. "Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications," Institute of Transportation Studies, Working Paper Series qt1f7661b4, Institute of Transportation Studies, UC Davis.
    8. Qinaat Hussain & Charitha Dias & Ali Al-Shahrani & Intizar Hussain, 2022. "Safety Analysis of Merging Vehicles Based on the Speed Difference between on-Ramp and Following Mainstream Vehicles Using NGSIM Data," Sustainability, MDPI, vol. 14(24), pages 1-12, December.
    9. Xingliang Liu & Jian Wang & Tangzhi Liu & Jin Xu, 2021. "Forecasting Spatiotemporal Boundary of Emergency-Event-Based Traffic Congestion in Expressway Network Considering Highway Node Acceptance Capacity," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    10. Hao, Peng & Wang, Chao, 2018. "Evaluating Environmental Impact of Traffic Congestion in Real Time Based on Sparse Mobile Crowd-sourced Data," Institute of Transportation Studies, Working Paper Series qt7q6760rz, Institute of Transportation Studies, UC Davis.

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