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Travel Time Estimation for Urban Arterials Based on the Multi-Source Data

Author

Listed:
  • Lingyu Zheng

    (College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China)

  • Hao Ma

    (Ningxia Branch, China Development Bank, Yinchuan 750002, China)

  • Zhongyu Wang

    (College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China)

Abstract

Accurate traffic information, such as travel time, becomes more important since it could help provide more efficient traffic management strategies. This paper presents a method for estimating the travel time of segments on urban arterials by leveraging multi-source data from loop detectors and probe vehicles. Travel time is defined into three distinct sections based on floating car trajectories, i.e., accelerating, constant speed, and decelerating. Considering the traffic flow characteristics, different methods are developed using various data for each section. The proposed methodology is validated using field data collected in Shanghai, China. The results validated the proposed method with absolute percentage errors (APEs) of approximately 5% in constrained traffic flow conditions and 10–20% in less constrained traffic flow. The results also show that the proposed method has better performance than the method with loop detector data and another data fusion model. It is expected that the proposed method could help improve traffic management efficiency, such as traffic signal control, by providing more accurate travel time information.

Suggested Citation

  • Lingyu Zheng & Hao Ma & Zhongyu Wang, 2024. "Travel Time Estimation for Urban Arterials Based on the Multi-Source Data," Sustainability, MDPI, vol. 16(17), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7845-:d:1474193
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    References listed on IDEAS

    as
    1. Tang, Jinjun & Yang, Yifan & Qi, Yong, 2018. "A hybrid algorithm for Urban transit schedule optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 745-755.
    2. Zong, Fang & Tian, Yongda & He, Yanan & Tang, Jinjun & Lv, Jianyu, 2019. "Trip destination prediction based on multi-day GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 258-269.
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