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Queue length estimation from probe vehicle location and the impacts of sample size

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  • Comert, Gurcan
  • Cetin, Mecit

Abstract

Probe vehicles are increasingly receiving more attention as an alternative means of collecting real-time traffic data needed for system optimization. This paper focuses on real-time estimation of queue lengths from the location information of probe vehicles in a queue at an isolated and undersaturated intersection. The paper also addresses the evaluation of the accuracy of such estimates as a function of the market penetration of probe vehicles. An analytical formulation based on conditional probability distributions is developed for estimating the expected queue length and its variance. It is found that, for the given settings, only the location information of the last probe vehicle in the queue is sufficient for the estimation. Exact expressions for the conditional mean and variance of queue length are derived. Various numerical results are documented to show how estimation errors behave by the volume to capacity ratio and by market penetration.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:197:y:2009:i:1:p:196-202
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. 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.
    3. Comert, Gurcan, 2013. "Effect of stop line detection in queue length estimation at traffic signals from probe vehicles data," European Journal of Operational Research, Elsevier, vol. 226(1), pages 67-76.
    4. Yang, Qiaoli & Shi, Zhongke & Yu, Shaowei & Zhou, Jie, 2018. "Analytical evaluation of the use of left-turn phasing for single left-turn lane only," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 266-303.
    5. Sara Sasaninejad & Joris Van Malderen & Joris Walraevens & Sabine Wittevrongel, 2023. "Expected Waiting Times at an Intersection with a Green Extension Strategy for Freight Vehicles: An Analytical Analysis," Mathematics, MDPI, vol. 11(3), pages 1-26, February.
    6. Xingmin Wang & Zachary Jerome & Zihao Wang & Chenhao Zhang & Shengyin Shen & Vivek Vijaya Kumar & Fan Bai & Paul Krajewski & Danielle Deneau & Ahmad Jawad & Rachel Jones & Gary Piotrowicz & Henry X. L, 2024. "Traffic light optimization with low penetration rate vehicle trajectory data," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. 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.
    8. 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.
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    14. Bae, Bumjoon & Liu, Yuandong & Han, Lee D. & Bozdogan, Hamparsum, 2019. "Spatio-temporal traffic queue detection for uninterrupted flows," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 20-34.

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