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Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

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  • Sida Luo

    (Department of Civil and Environmental Engineering, Northwestern University, 2145 Sheridan Road Evanston, IL 60208,United States)

Abstract

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

Suggested Citation

  • Sida Luo, 2020. "Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(02), pages 1-22, February.
    • Handle: RePEc:wsi:ijmpcx:v:31:y:2020:i:02:n:s0129183120500230
    DOI: 10.1142/S0129183120500230
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    Cited by:

    1. Muren, & Zhang, Shiyuan & Hua, Lianlian & Yu, Bo, 2022. "Peak-easing strategies for urban subway operations in the context of COVID-19 epidemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).

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