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A Microscopic Investigation Into the Capacity Drop: Impacts of Longitudinal Behavior on the Queue Discharge Rate

Author

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  • Kai Yuan

    (Delft University of Technology, 2628 CN Delft, Netherlands)

  • Victor L. Knoop

    (Delft University of Technology, 2628 CN Delft, Netherlands)

  • Serge P. Hoogendoorn

    (Delft University of Technology, 2628 CN Delft, Netherlands)

Abstract

The capacity drop indicates that the queue discharge rate is lower than the free-flow capacity. Studies show that the queue discharge rate varies under different traffic conditions. Empirical data show that the queue discharge rate increases as the speed in congestion increases. Insights into the underlying behavioral mechanisms that cause these variable queue discharge rates can help minimize traffic delays and eliminate congestion. However, to our knowledge, few efforts have been devoted to testing impacts of traffic behaviors on the queue discharge rate. This paper tries to fill this gap. We investigate to what extent the acceleration spread and reaction time can influence the queue discharge rate. It is found that the (interdriver) acceleration spread does not reduce the queue discharge rates as much as found empirically. Modeling reaction time might be more important than modeling acceleration for capacity drop in car-following models. A speed-dependent reaction time mechanism for giving variable queue discharge rates is proposed. That is, decreasing reaction time as the speed in congestion increases can give the same queue discharge rate as found empirically. This research suggests that motivating drivers to speed up earlier could increase the queue discharge rate and thereby minimize delays.

Suggested Citation

  • Kai Yuan & Victor L. Knoop & Serge P. Hoogendoorn, 2017. "A Microscopic Investigation Into the Capacity Drop: Impacts of Longitudinal Behavior on the Queue Discharge Rate," Transportation Science, INFORMS, vol. 51(3), pages 852-862, August.
    • Handle: RePEc:inm:ortrsc:v:51:y:2017:i:3:p:852-862
    DOI: 10.287/trsc.2017.0745
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    References listed on IDEAS

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    Cited by:

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    3. Wang, Jiawen & Zou, Linzhi & Zhao, Jing & Wang, Xinwei, 2024. "Dynamic capacity drop propagation in incident-affected networks: Traffic state modeling with SIS-CTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    4. Tang, Qing & Hu, Xianbiao & Lu, Jiawei & Zhou, Xuesong, 2021. "Analytical characterization of multi-state effective discharge rates for bus-only lane conversion scheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 106-131.
    5. Wang, Xiao & Jiang, Rui & Li, Li & Lin, Yi-Lun & Wang, Fei-Yue, 2019. "Long memory is important: A test study on deep-learning based car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 786-795.

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