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Optimal occupancy-driven parking pricing under demand uncertainties and traveler heterogeneity: A stochastic control approach

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  • Qian, Zhen (Sean)
  • Rajagopal, Ram

Abstract

A novel parking pricing strategy dependent on real-time sensing is proposed to manage the parking demand. Parking pricing and information provision jointly serve as a dynamic stabilized controller to minimize the total travel time (TTT) of the system. Parking prices are adjusted in real time according to the real-time occupancy collected by parking sensors. All the parking information along with parking prices, is then provided for travelers to make real-time parking choices. We model the optimal parking pricing in the preferred (closer) parking cluster as a stochastic control problem. We take into account two types of randomness, demand uncertainties and user heterogeneity in Value of Time (VOT), both of which can be learned by taking real-time measurements. The optimal parking pricing policies are solved using the dynamic programming approach. There exists a critical occupancy for each time period, and the parking prices should be set effective (by diverting travelers to the farther parking lot) when the up-to-date occupancy is above the critical occupancy. From the numerical experiments, we find that the optimal parking policies based on stochastic control models are promising. They can deal with different demand levels (high, low or unstable) and generally outperform the deterministic pricing schemes. It can approach the minimum TTT in most of the cases as if we know the traffic demand in advance of the commuting time. Providing real-time occupancy information alone without setting proper parking prices, seems useful, but marginal, in reducing the parking congestion.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transb:v:67:y:2014:i:c:p:144-165
    DOI: 10.1016/j.trb.2014.03.002
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    References listed on IDEAS

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    Cited by:

    1. Karaliopoulos, Merkouris & Katsikopoulos, Konstantinos & Lambrinos, Lambros, 2017. "Bounded rationality can make parking search more efficient: The power of lexicographic heuristics," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 28-50.
    2. 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.
    3. 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.
    4. Shao, Saijun & Xu, Su Xiu & Yang, Hai & Huang, George Q., 2020. "Parking reservation disturbances," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 83-97.
    5. Cats, Oded & Zhang, Chen & Nissan, Albania, 2016. "Survey methodology for measuring parking occupancy: Impacts of an on-street parking pricing scheme in an urban center," Transport Policy, Elsevier, vol. 47(C), pages 55-63.
    6. Xu, Su Xiu & Cheng, Meng & Kong, Xiang T.R. & Yang, Hai & Huang, George Q., 2016. "Private parking slot sharing," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 596-617.
    7. Boyles, Stephen D. & Tang, Shoupeng & Unnikrishnan, Avinash, 2015. "Parking search equilibrium on a network," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 390-409.
    8. Lin, XuXun & Yuan, PengCheng, 2018. "A dynamic parking charge optimal control model under perspective of commuters’ evolutionary game behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1096-1110.
    9. 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.
    10. Zou, Bo & Kafle, Nabin & Wolfson, Ouri & Lin, Jie (Jane), 2015. "A mechanism design based approach to solving parking slot assignment in the information era," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 631-653.
    11. Igor Lazov, 2019. "A Methodology for Revenue Analysis of Parking Lots," Networks and Spatial Economics, Springer, vol. 19(1), pages 177-198, March.
    12. Pi, Xidong & Qian, Zhen (Sean), 2017. "A stochastic optimal control approach for real-time traffic routing considering demand uncertainties and travelers’ choice heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 710-732.
    13. 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.
    14. Sayarshad, Hamid R. & Sattar, Shahram & Oliver Gao, H., 2020. "A scalable non-myopic atomic game for a smart parking mechanism," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).

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