IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v194y2025ics1366554524004782.html
   My bibliography  Save this article

Online configuration of reservable parking spaces: An agent-based deep reinforcement learning approach

Author

Listed:
  • Xie, Minghui
  • Lin, Siyu
  • Wei, Sen
  • Zhang, Xinying
  • Wang, Yao
  • Wang, Yuanqing

Abstract

Unevenly distributed parking demand frequently leads to the overconsumption of popular parking lots, resulting in increased regional travel costs and traffic congestion. Configuring reservable parking spaces in parking lots based on online reservation systems is a prevalent solution to alleviate these issues. However, existing static configuration methods are inadequate for addressing time-varying parking demand, presenting significant challenges in determining the optimal number of reservable parking spaces across different parking lots over time. Thus, to address these challenges and reduce the total travel time in popular reservation-enabled management areas, this paper proposes a dynamic configuration model for reservable parking spaces utilizing agent-based deep reinforcement learning. The model can dynamically schedule the ratio of reservable parking spaces in an environment where reserved users and non-reserved users coexist, thereby influencing parking users’ choice behavior and balancing demand distribution. Experimental results on a real-world simulator show that, compared to baseline methods, the proposed model can effectively configure reservable parking spaces online. It conservatively reduces the total travel time by 21.4% and alleviates parking cruising and waiting in the management area. This approach is prospective for smart parking management.

Suggested Citation

  • Xie, Minghui & Lin, Siyu & Wei, Sen & Zhang, Xinying & Wang, Yao & Wang, Yuanqing, 2025. "Online configuration of reservable parking spaces: An agent-based deep reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:transe:v:194:y:2025:i:c:s1366554524004782
    DOI: 10.1016/j.tre.2024.103887
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554524004782
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2024.103887?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. Shoup, Donald C., 2006. "Cruising for Parking," University of California Transportation Center, Working Papers qt55s7079f, University of California Transportation Center.
    2. Mei, Zhenyu & Feng, Chi & Ding, Wenchao & Zhang, Lihui & Wang, Dianhai, 2019. "Better lucky than rich? Comparative analysis of parking reservation and parking charge," Transport Policy, Elsevier, vol. 75(C), pages 47-56.
    3. Jiang, Bowen & Fan, Zhi-Ping, 2020. "Optimal allocation of shared parking slots considering parking unpunctuality under a platform-based management approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    4. Bahrami, Sina & Roorda, Matthew, 2022. "Autonomous vehicle parking policies: A case study of the City of Toronto," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 283-296.
    5. Wang, Xiaotian & Wang, Xin, 2019. "Flexible parking reservation system and pricing: A continuum approximation approach," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 408-434.
    6. Shoup, Donald C., 2006. "Cruising for parking," Transport Policy, Elsevier, vol. 13(6), pages 479-486, November.
    7. Fabusuyi, Tayo & Hampshire, Robert C., 2018. "Rethinking performance based parking pricing: A case study of SFpark," Transportation Research Part A: Policy and Practice, Elsevier, vol. 115(C), pages 90-101.
    8. Yang, Hai & Liu, Wei & Wang, Xiaolei & Zhang, Xiaoning, 2013. "On the morning commute problem with bottleneck congestion and parking space constraints," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 106-118.
    9. Wang, Yineng & Li, Meng & Lin, Xi & He, Fang, 2021. "Online operations strategies for automated multistory parking facilities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    10. Tian, Qiong & Yang, Li & Wang, Chenlan & Huang, Hai-Jun, 2018. "Dynamic pricing for reservation-based parking system: A revenue management method," Transport Policy, Elsevier, vol. 71(C), pages 36-44.
    11. Robert C. Hampshire & Donald Shoup, 2018. "What Share of Traffic is Cruising for Parking?," Journal of Transport Economics and Policy, University of Bath, vol. 52(3), pages 184-18-201.
    12. Najmi, Ali & Bostanara, Maryam & Gu, Ziyuan & Rashidi, Taha H., 2021. "On-street parking management and pricing policies: An evaluation from a system enhancement perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 128-151.
    13. Xie, Minghui & Zhang, Xinying & Wu, Zhouhao & Wei, Sen & Gao, Yanan & Wang, Yuanqing, 2023. "A shared parking optimization framework based on dynamic resource allocation and path planning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    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. Lu, Xiao-Shan & Guo, Ren-Yong & Huang, Hai-Jun & Xu, Xiaoming & Chen, Jiajia, 2021. "Equilibrium analysis of parking for integrated daily commuting," Research in Transportation Economics, Elsevier, vol. 90(C).
    2. Lu, Xiao-Shan & Huang, Hai-Jun & Guo, Ren-Yong & Xiong, Fen, 2021. "Linear location-dependent parking fees and integrated daily commuting patterns with late arrival and early departure in a linear city," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 293-322.
    3. Xie, Minghui & Zhang, Xinying & Wu, Zhouhao & Wei, Sen & Gao, Yanan & Wang, Yuanqing, 2023. "A shared parking optimization framework based on dynamic resource allocation and path planning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    4. Yu, Hao & Huang, Min & Song, Yang & Wang, Xingwei & Yue, Xiaohang, 2025. "Making the most of your private parking slot: Strategy-proof double auctions-enabled staggered sharing schemes," Transportation Research Part B: Methodological, Elsevier, vol. 191(C).
    5. Zhang, Xinying & Pitera, Kelly & Wang, Yuanqing, 2024. "Exploring parking choices under the coexistence of autonomous and conventional vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    6. Fu, Yulan & Wang, Chenlan & Liu, Tian-Liang & Huang, Hai-Jun, 2021. "Parking management in the morning commute problem with ridesharing," Research in Transportation Economics, Elsevier, vol. 90(C).
    7. Tang, Zhe-Yi & Tian, Li-Jun & Wang, David Z.W., 2021. "Multi-modal morning commute with endogenous shared autonomous vehicle penetration considering parking space constraint," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    8. Sowmya Karri & Meera M. Dhabu, 2022. "Multistage Game Model Based Dynamic Pricing for Car Parking Slot to Control Congestion," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    9. Simona Mikšíková & David Ulčák & František Kuda, 2022. "Analysis of Malfunctions in Selected Parking Systems in the Czech Republic," Sustainability, MDPI, vol. 14(3), pages 1-10, February.
    10. Feng, Jianghong & Xu, Su Xiu & Xu, Gangyan & Cheng, Huibing, 2022. "An integrated decision-making method for locating parking centers of recyclable waste transportation vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    11. Wang, Pengfei & Guan, Hongzhi & Liu, Peng, 2020. "Modeling and solving the optimal allocation-pricing of public parking resources problem in urban-scale network," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 74-98.
    12. Gu, Ziyuan & Li, Yifan & Saberi, Meead & Rashidi, Taha H. & Liu, Zhiyuan, 2023. "Macroscopic parking dynamics and equitable pricing: Integrating trip-based modeling with simulation-based robust optimization," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 354-381.
    13. Liu, Wei & Geroliminis, Nikolas, 2016. "Modeling the morning commute for urban networks with cruising-for-parking: An MFD approach," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 470-494.
    14. He, Fang & Yin, Yafeng & Chen, Zhibin & Zhou, Jing, 2015. "Pricing of parking games with atomic players," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 1-12.
    15. Gu, Ziyuan & Safarighouzhdi, Farshid & Saberi, Meead & Rashidi, Taha H., 2021. "A macro-micro approach to modeling parking," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 220-244.
    16. Navid Nadimi & Mohammad Ali Zayandehroodi & Rosalia Camporeale & Morteza Asadamraji, 2023. "A Framework for Providing Information about Parking Spaces," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    17. Jin Cao & Monica Menendez & Rashid Waraich, 2019. "Impacts of the urban parking system on cruising traffic and policy development: the case of Zurich downtown area, Switzerland," Transportation, Springer, vol. 46(3), pages 883-908, June.
    18. Zipeng Zhang & Ning Zhang, 2021. "Early Bird Scheme for Parking Management: How Does Parking Play a Role in the Morning Commute Problem," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    19. Xiao, Jun & Lou, Yingyan & Frisby, Joshua, 2018. "How likely am I to find parking? – A practical model-based framework for predicting parking availability," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 19-39.
    20. Zhibin Chen & Stephen Spana & Yafeng Yin & Yuchuan Du, 2019. "An Advanced Parking Navigation System for Downtown Parking," Networks and Spatial Economics, Springer, vol. 19(3), pages 953-968, September.

    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:eee:transe:v:194:y:2025:i:c:s1366554524004782. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.