IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v343y2024i3d10.1007_s10479-022-04734-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04734-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-022-04734-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Coogan, Samuel & Flores, Christopher & Varaiya, Pravin, 2017. "Traffic predictive control from low-rank structure," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 1-22.
    2. Abdullah Alshehri & Mahmoud Owais & Jayadev Gyani & Mishal H. Aljarbou & Saleh Alsulamy, 2023. "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    3. Ng, ManWo, 2012. "Synergistic sensor location for link flow inference without path enumeration: A node-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 781-788.
    4. Mínguez, R. & Sánchez-Cambronero, S. & Castillo, E. & Jiménez, P., 2010. "Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 282-298, February.
    5. Beatriz Molina Serrano & Nicoleta González-Cancelas & Francisco Soler-Flores & Samir Awad-Nuñez & Alberto Camarero Orive, 2018. "Use of Bayesian Networks to Analyze Port Variables in Order to Make Sustainable Planning and Management Decision," Logistics, MDPI, vol. 2(1), pages 1-16, January.
    6. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar, 2008. "Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 455-481, June.
    7. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.
    8. Maosheng Li & Qian Luo & Jing Fan & Qingyan Ning, 2023. "Impact Analysis of Smart Road Stud on Driving Behavior and Traffic Flow in Two-Lane Two-Way Highway," Sustainability, MDPI, vol. 15(15), pages 1-20, July.
    9. Kumar, Anshuman Anjani & Kang, Jee Eun & Kwon, Changhyun & Nikolaev, Alexander, 2016. "Inferring origin-destination pairs and utility-based travel preferences of shared mobility system users in a multi-modal environment," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 270-291.
    10. Yinglian Zhou & Jifeng Chen, 2021. "Traffic Change Forecast and Decision Based on Variable Structure Dynamic Bayesian Network: Traffic Decision," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 13(2), pages 1-17, April.
    11. Wen, Tzai-Hung & Chin, Wei-Chien-Benny & Lai, Pei-Chun, 2017. "Understanding the topological characteristics and flow complexity of urban traffic congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 166-177.
    12. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    13. Mrinal Kanti Sen & Subhrajit Dutta & Golam Kabir, 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    14. Xinhua Mao & Changwei Yuan & Jiahua Gan, 2019. "Incorporating Dynamic Traffic Distribution into Pavement Maintenance Optimization Model," Sustainability, MDPI, vol. 11(9), pages 1-15, April.
    15. Zheng Zhu & Xiqun Chen & Chenfeng Xiong & Lei Zhang, 2018. "A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice," Transportation, Springer, vol. 45(5), pages 1499-1522, September.
    16. Julio, Nikolas & Giesen, Ricardo & Lizana, Pedro, 2016. "Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms," Research in Transportation Economics, Elsevier, vol. 59(C), pages 250-257.
    17. Yanbing Li & Wei Zhao & Huilong Fan, 2022. "A Spatio-Temporal Graph Neural Network Approach for Traffic Flow Prediction," Mathematics, MDPI, vol. 10(10), pages 1-14, May.
    18. Siripirote, Treerapot & Sumalee, Agachai & Ho, H.W. & Lam, William H.K., 2015. "Statistical approach for activity-based model calibration based on plate scanning and traffic counts data," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 280-300.
    19. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2015. "Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 27-47.
    20. Yulong Pei & Songmin Ran & Wanjiao Wang & Chuntong Dong, 2023. "Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM," Sustainability, MDPI, vol. 15(20), pages 1-20, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:343:y:2024:i:3:d:10.1007_s10479-022-04734-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.