IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i12p4864-d371585.html
   My bibliography  Save this article

Neural-Network-Based Dynamic Distribution Model of Parking Space Under Sharing and Non-Sharing Modes

Author

Listed:
  • Ziyao Zhao

    (Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China)

  • Yi Zhang

    (Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
    Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
    Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China)

  • Yi Zhang

    (Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
    Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Kaifeng Ji

    (Industrial Engineering department, Tsinghua University, Beijing 100084, China)

  • He Qi

    (China Construction Science and Technology Co. Ltd., Shenzhen 510152, China)

Abstract

In recent years, with the rapid development of China’s automobile industry, the number of vehicles in China has been increasing steadily. Vehicles represent a convenient mode of travel, but the growth rate of the number of urban motor vehicles far exceeds the construction rate of parking facilities. The continuous improvement of parking allocation methods has always been key for ensuring sustainable city management. Thus, developing an efficient and dynamic parking distribution algorithm will be an important breakthrough to alleviate the urban parking shortage problem. However, the existing parking distribution models do not adequately consider the influence of real-time changes in parking demand and supply on parking space assignment. Therefore, this study proposed a method for dynamic parking allocation using parking demand predictions and a predictive control method. A neural-network-based dynamic parking distribution model was developed considering seven influencing factors: driving duration, walking distance, parking fee, traffic congestion, possibility of finding a parking space in the target parking lot and adjacent parking lot, and parking satisfaction degree. Considering whether the parking spaces in the targeted parking lots are shared or not, two allocation modes—sharing mode and non-sharing mode—were proposed and embedded into the model. At the experimental stage, a simulation case and a real-time case were performed to evaluate the developed models. The experimental results show that the dynamic parking distribution model based on neural networks can not only allocate parking spaces in real time but also improve the utilisation rate of different types of parking spaces. The performance score of the dynamic parking distribution model for a time interval of 2–20 min was maintained above 80%. In addition, the distribution performance of the sharing mode was better than that of the non-sharing mode and contributed to a better overall effectiveness. This model can effectively improve the utilisation rate of resources and the uniformity of distribution and can reduce the failure rate of parking; thus, it significantly contributes to more smart and sustainable urban parking management.

Suggested Citation

  • Ziyao Zhao & Yi Zhang & Yi Zhang & Kaifeng Ji & He Qi, 2020. "Neural-Network-Based Dynamic Distribution Model of Parking Space Under Sharing and Non-Sharing Modes," Sustainability, MDPI, vol. 12(12), pages 1-27, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:4864-:d:371585
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/12/4864/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/12/4864/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lam, William H.K. & Li, Zhi-Chun & Huang, Hai-Jun & Wong, S.C., 2006. "Modeling time-dependent travel choice problems in road networks with multiple user classes and multiple parking facilities," Transportation Research Part B: Methodological, Elsevier, vol. 40(5), pages 368-395, June.
    2. van Ommeren, Jos & de Groote, Jesper & Mingardo, Giuliano, 2014. "Residential parking permits and parking supply," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 33-44.
    3. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
    4. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-260, May.
    5. Shoup, Donald, 2007. "Cruising for Parking," University of California Transportation Center, Working Papers qt6sn7s1x2, University of California Transportation Center.
    6. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1991. "A temporal and spatial equilibrium analysis of commuter parking," Journal of Public Economics, Elsevier, vol. 45(3), pages 301-335, August.
    7. Arnott, Richard & Rowse, John, 2009. "Downtown parking in auto city," Regional Science and Urban Economics, Elsevier, vol. 39(1), pages 1-14, January.
    8. Andrew Kelly, J. & Peter Clinch, J., 2006. "Influence of varied parking tariffs on parking occupancy levels by trip purpose," Transport Policy, Elsevier, vol. 13(6), pages 487-495, November.
    9. Teodorovic, Dusan & Lucic, Panta, 2006. "Intelligent parking systems," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1666-1681, December.
    10. Michael W. Mehaffy & Sergio Porta & Ombretta Romice, 2015. "The "neighborhood unit" on trial: a case study in the impacts of urban morphology," Journal of Urbanism: International Research on Placemaking and Urban Sustainability, Taylor & Francis Journals, vol. 8(2), pages 199-217, June.
    11. Zhang, Xiaoning & Huang, Hai-Jun & Zhang, H.M., 2008. "Integrated daily commuting patterns and optimal road tolls and parking fees in a linear city," Transportation Research Part B: Methodological, Elsevier, vol. 42(1), pages 38-56, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jun Li & Sifan Wu & Xiaoman Feng, 2021. "Optimization of On-Street Parking Charges Based on Price Elasticity of the Expected Perceived Parking Cost," Sustainability, MDPI, vol. 13(10), pages 1-13, May.

    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. Inci, Eren, 2015. "A review of the economics of parking," Economics of Transportation, Elsevier, vol. 4(1), pages 50-63.
    2. Qian, Zhen (Sean) & Rajagopal, Ram, 2014. "Optimal occupancy-driven parking pricing under demand uncertainties and traveler heterogeneity: A stochastic control approach," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 144-165.
    3. 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.
    4. Amer, Ahmed & Chow, Joseph Y.J., 2017. "A downtown on-street parking model with urban truck delivery behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 51-67.
    5. Wei Wu & Wei Liu & Fangni Zhang & Vinayak Dixit, 2021. "A New Flexible Parking Reservation Scheme for the Morning Commute under Limited Parking Supplies," Networks and Spatial Economics, Springer, vol. 21(3), pages 513-545, September.
    6. André de Palma & Mogens Fosgerau, 2011. "Dynamic Traffic Modeling," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 9, Edward Elgar Publishing.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Kobus, Martijn B.W. & Gutiérrez-i-Puigarnau, Eva & Rietveld, Piet & Van Ommeren, Jos N., 2013. "The on-street parking premium and car drivers' choice between street and garage parking," Regional Science and Urban Economics, Elsevier, vol. 43(2), pages 395-403.
    13. Geroliminis, Nikolas, 2015. "Cruising-for-parking in congested cities with an MFD representation," Economics of Transportation, Elsevier, vol. 4(3), pages 156-165.
    14. Takayama, Yuki & Kuwahara, Masao, 2016. "Scheduling preferences, parking competition, and bottleneck congestion: A model of trip timing and parking location choices by heterogeneous commuters," MPRA Paper 68938, University Library of Munich, Germany.
    15. Wang, Jing & Zhang, Xiaoning & Wang, Hua & Zhang, Michael, 2019. "Optimal parking supply in bi-modal transportation network considering transit scale economies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 207-229.
    16. Li, Zhi-Chun & Huang, Hai-Jun & Yang, Hai, 2020. "Fifty years of the bottleneck model: A bibliometric review and future research directions," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 311-342.
    17. Gallo, Mariano & D'Acierno, Luca & Montella, Bruno, 2011. "A multilayer model to simulate cruising for parking in urban areas," Transport Policy, Elsevier, vol. 18(5), pages 735-744, September.
    18. Ling-Ling Xiao & Tian-Liang Liu & Hai-Jun Huang, 2021. "Tradable permit schemes for managing morning commute with carpool under parking space constraint," Transportation, Springer, vol. 48(4), pages 1563-1586, August.
    19. De Borger, Bruno & Russo, Antonio, 2017. "The political economy of pricing car access to downtown commercial districts," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 76-93.
    20. 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.

    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:gam:jsusta:v:12:y:2020:i:12:p:4864-:d:371585. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.