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Dynamic pricing for reservation-based parking system: A revenue management method

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  • Tian, Qiong
  • Yang, Li
  • Wang, Chenlan
  • Huang, Hai-Jun

Abstract

With the improvement of urban smart level, parking reservation has become not only one of the most effective ways to solve parking problems but also an efficient tool to reduce traffic congestion in eliminating the cruising. This paper proposes a new dynamic pricing model for parking reservation, aiming to maximize the expected revenue of the parking manager. The parking requests arrive as a Poisson process, and the arrival intensity is influenced by the time-varying parking price. The optimal pricing scheme is proved to be unique for any general demand and derived in closed form for some particular types of demand functions, like exponential and linear. Numerical examples show that the dynamic pricing scheme can provide significant improvement in revenue and make full use of the parking resources during peak periods.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:trapol:v:71:y:2018:i:c:p:36-44
    DOI: 10.1016/j.tranpol.2018.07.007
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. Mark Friesen & Giuliano Mingardo, 2020. "Is Parking in Europe Ready for Dynamic Pricing? A Reality Check for the Private Sector," Sustainability, MDPI, vol. 12(7), pages 1-11, March.
    4. Xiao, Haohan & Xu, Min & Wang, Shuaian, 2024. "Auction-based parking mechanisms considering withdrawal behaviors," Transport Policy, Elsevier, vol. 147(C), pages 81-93.
    5. Huang, Hai-Jun & Xia, Tian & Tian, Qiong & Liu, Tian-Liang & Wang, Chenlan & Li, Daqing, 2020. "Transportation issues in developing China's urban agglomerations," Transport Policy, Elsevier, vol. 85(C), pages 1-22.
    6. 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.
    7. Ren, Tao & Huang, Hai-Jun, 2020. "A competitive system with transit and highway: Revisiting the political feasibility of road pricing," Transport Policy, Elsevier, vol. 88(C), pages 42-56.
    8. Vital, Filipe & Ioannou, Petros, 2022. "Balancing of Truck Parking Demand by a Centralized Incentives/Pricing System," Institute of Transportation Studies, Working Paper Series qt3zv2s5jr, Institute of Transportation Studies, UC Davis.
    9. 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.
    10. 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).
    11. Zhou, Xizhen & Lv, Mengqi & Ji, Yanjie & Zhang, Shuichao & Liu, Yong, 2023. "Pricing curb parking: Differentiated parking fees or cash rewards?," Transport Policy, Elsevier, vol. 142(C), pages 46-58.
    12. Huanmei Qin & Ning Xu & Yonghuan Zhang & Qianqian Pang & Zhaolin Lu, 2023. "Research on Parking Recommendation Methods Considering Travelers’ Decision Behaviors and Psychological Characteristics," Sustainability, MDPI, vol. 15(8), pages 1-22, April.

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