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Dynamic macroscopic simulation of on-street parking search: A trip-based approach

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  • Leclercq, Ludovic
  • Sénécat, Alméria
  • Mariotte, Guilhem

Abstract

This paper extends a trip-based aggregate dynamic traffic model to account for on-street parking search. The trip-based approach for a road network defined as a reservoir characterizes the internal traffic states by a macroscopic fundamental diagram (MFD) in speed while individualizing all vehicle travel distances. This paper first investigates distances to park for on-street parking based on real data in Lyon (France) and stochastic numerical experiments. An updated formulation compared to the existing literature is proposed for the relation between such distances and the parking occupancy. This new formulation is then incorporated into an event-based numerical scheme that solves the trip-based MFD model. The complete framework is able to account for different vehicle categories with respect to their parking strategies and to finely tune the related travel distances. Finally, the capabilities of the full framework are illustrated based on three different scenarios. The first two correspond to strategies with static and dynamic (reactive) switch of the demand from on- to off-street parking. While being very classical, they permit to demonstrate that the proposed model reacts as expected in such cases. The third scenario assesses the effect of a smart-parking technology that informs the users when a free parking spot is available on one of the downstream links at each intersection. In such a case, the model permits to estimate the benefit for the equipped users but also the impacts on all other vehicle categories. The three scenarios highlight that the proposed framework is versatile and can quickly provide a first assessment with a low calibration burden of different parking strategies or policies.

Suggested Citation

  • Leclercq, Ludovic & Sénécat, Alméria & Mariotte, Guilhem, 2017. "Dynamic macroscopic simulation of on-street parking search: A trip-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 268-282.
    • Handle: RePEc:eee:transb:v:101:y:2017:i:c:p:268-282
    DOI: 10.1016/j.trb.2017.04.004
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    References listed on IDEAS

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    Cited by:

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    2. Aghamohammadi, Rafegh & Laval, Jorge A., 2020. "A continuum model for cities based on the macroscopic fundamental diagram: A semi-Lagrangian solution method," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 101-116.
    3. Mourtakos, Vasileios & Mantouka, Eleni G. & Fafoutellis, Panagiotis & Vlahogianni, Eleni I. & Kepaptsoglou, Konstantinos, 2024. "Reconstructing mobility from smartphone data: Empirical evidence of the effects of COVID-19 pandemic crisis on working and leisure," Transport Policy, Elsevier, vol. 146(C), pages 241-254.
    4. 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).
    5. Semeneh Hunachew Bayih & Surafel Luleseged Tilahun, 2024. "Dynamic vehicle parking pricing. A review," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(1), pages 35-59.
    6. 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.
    7. 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.
    8. Xiaojuan Yu & Vincent A.C. van den Berg, 2024. "Human-driven vehicles’ cruising versus autonomous vehicles’ back- and-forth congestion: The effects on traveling, parking and congestion," Tinbergen Institute Discussion Papers 24-032/VIII, Tinbergen Institute.
    9. Laval, Jorge A. & Aghamohammadi, Rafegh, 2022. "Network-wide Emissions Estimation Using the Macroscopic Fundamental Diagram," Institute of Transportation Studies, Working Paper Series qt8670m9jh, Institute of Transportation Studies, UC Davis.
    10. Niu, Xiao-Jing & Zhao, Xiao-Mei & Xie, Dong-Fan & Liu, Feng & Bi, Jun & Lu, Chaoru, 2022. "Impact of large-scale activities on macroscopic fundamental diagram: Field data analysis and modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 241-268.
    11. Abdelghaffar, Hossam M. & Batista, S.F.A. & Rehman, Abdur & Cao, Jin & Menéndez, Mónica & Jabari, Saif Eddin, 2024. "Comparison of probabilistic cruising-for-parking time estimation models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    12. Mariotte, Guilhem & Leclercq, Ludovic, 2019. "Flow exchanges in multi-reservoir systems with spillbacks," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 327-349.
    13. Zhong, R.X. & Huang, Y.P. & Chen, C. & Lam, W.H.K. & Xu, D.B. & Sumalee, A., 2018. "Boundary conditions and behavior of the macroscopic fundamental diagram based network traffic dynamics: A control systems perspective," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 327-355.
    14. Lu, Xiao-Shan & Guo, Ren-Yong & Huang, Hai-Jun & Ding, Heng, 2024. "Potential-based dynamic parking navigation for autonomous vehicles: Near-priority vs. distant-priority," Transport Policy, Elsevier, vol. 155(C), pages 79-92.
    15. Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2017. "Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 193-211.
    16. 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.
    17. Tan, Bing Qing & Xu, Su Xiu & Thürer, Matthias & Kang, Kai & Zhao, Zhiheng & Li, Ming, 2024. "Booking versus search-based parking strategy: A game-theoretic methodology," Research in Transportation Economics, Elsevier, vol. 104(C).
    18. Johari, Mansour & Keyvan-Ekbatani, Mehdi, 2024. "Macroscopic modeling of mixed bi-modal urban networks: A hybrid model of accumulation- and trip-based principles," Transportation Research Part B: Methodological, Elsevier, vol. 182(C).
    19. Liu, Wei & Szeto, Wai Yuen, 2020. "Learning and managing stochastic network traffic dynamics with an aggregate traffic representation," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 19-46.
    20. Batista, S.F.A. & Leclercq, Ludovic & Geroliminis, Nikolas, 2019. "Estimation of regional trip length distributions for the calibration of the aggregated network traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 192-217.
    21. Clélia Lopez & Chuan-Lin Zhao & Stéphane Magniol & Nicolas Chiabaut & Ludovic Leclercq, 2019. "Microscopic Simulation of Cruising for Parking of Trucks as a Measure to Manage Freight Loading Zone," Sustainability, MDPI, vol. 11(5), pages 1-14, February.
    22. Ding, Heng & Qian, Yu & Zheng, Xiaoyan & Bai, Haijian & Wang, Shiguang & Zhou, Jingwen, 2022. "Dynamic parking charge–perimeter control coupled method for a congested road network based on the aggregation degree characteristics of parking generation distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    23. Paipuri, Mahendra & Leclercq, Ludovic, 2020. "Bi-modal macroscopic traffic dynamics in a single region," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 257-290.

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