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Understanding the uncertainty of traffic time prediction impacts on parking lot reservation in logistics centers

Author

Listed:
  • Rui Feng

    (Dalian University of Technology)

  • Ankun Ma

    (Dalian University of Technology)

  • Zhijia Jing

    (Dalian University of Technology)

  • Xiaoning Gu

    (Dalian University of Technology)

  • Pengfei Dang

    (Dalian University of Technology)

  • Baozhen Yao

    (Dalian University of Technology)

Abstract

Accurate travel time information is essential for logistics vehicles to reserve the most suitable parking lot in logistics centers. The purpose of this study is to explore how the uncertainty of traffic time prediction affects parking lot reservation near logistics centers. A hybrid model integrating convolutional long short-term memory network and attention mechanism is proposed to provide the reliable information for travel time prediction intervals. Furthermore, a reliability-based parking lot reservation model is developed by explicitly considering logistics vehicles’ time probability. Several benchmark models are compared with the proposed traffic speed prediction model. The performance of the parking lot reservation model is illustrated by travel behavior questionnaire data and global positioning system data of collected from Beijing, China. The results illustrate that the proposed prediction model exhibits a better accuracy than benchmark models. Moreover, it is found that travel time prediction interval can improve the reliability and stability of travel time, and provide a reliable time information for the parking lot reservation.

Suggested Citation

  • Rui Feng & Ankun Ma & Zhijia Jing & Xiaoning Gu & Pengfei Dang & Baozhen Yao, 2024. "Understanding the uncertainty of traffic time prediction impacts on parking lot reservation in logistics centers," Annals of Operations Research, Springer, vol. 343(3), pages 1045-1067, December.
    • Handle: RePEc:spr:annopr:v:343:y:2024:i:3:d:10.1007_s10479-022-04734-z
    DOI: 10.1007/s10479-022-04734-z
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    References listed on IDEAS

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    1. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
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