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Modelling parking choice behaviour using Possibility Theory

Author

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  • Michele Ottomanelli
  • Mauro Dell'Orco
  • Domenico Sassanelli

Abstract

This article presents a discrete choice model for evaluating parking users’ behaviour. In order to explicitly take into account imprecision and uncertainty underlying a user's choice process, the proposed model has been developed within the framework of Possibility Theory. This approach is an alternative way to represent imperfect knowledge (uncertainty) of users about both parking and transportation system status, as well as the approximate reasoning of the human decision maker (imprecision). The resulting model is a quantitative soft computing tool that could support traffic analysts in planning parking policies and Advanced Traveller Information Systems. In fact, effects of information on user choice can be incorporated into the model itself. Thus, we consider the parking user be a decision maker who assumes a certain choice set (set of perceived parking alternatives); the user has some information about the parking supply system and he/she associates each parking alternative with an approximate perceived cost/utility that is represented by a possibility distribution; and, finally, the user chooses the alternative which minimises/maximises his/her perceived parking cost/utility. The results show how the model is able to represent the effect of various parking policies on users’ behaviour and how the single component of parking policy affects the decision process.

Suggested Citation

  • Michele Ottomanelli & Mauro Dell'Orco & Domenico Sassanelli, 2011. "Modelling parking choice behaviour using Possibility Theory," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(7), pages 647-667, April.
  • Handle: RePEc:taf:transp:v:34:y:2011:i:7:p:647-667
    DOI: 10.1080/03081060.2011.602846
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    Cited by:

    1. 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).
    2. Antolín, Gonzalo & Ibeas, Ángel & Alonso, Borja & dell'Olio, Luigi, 2018. "Modelling parking behaviour considering users heterogeneities," Transport Policy, Elsevier, vol. 67(C), pages 23-30.
    3. 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.
    4. Nguyen Viet Long & Hoang Thuy Linh & Vu Anh Tuan, 2023. "Towards Smart Parking Management: Econometric Analysis and Modeling of Public-Parking-Choice Behavior in Three Cities of Binh Duong, Vietnam," Sustainability, MDPI, vol. 15(24), pages 1-22, December.
    5. Yunqiang Xue & Qifang Kong & Feng Sun & Meng Zhong & Haokai Tu & Caifeng Tan & Hongzhi Guan, 2022. "Shared Parking Decision Behavior of Parking Space Owners and Car Travelers Based on Prospect Theory—A Case Study of Nanchang City, China," Sustainability, MDPI, vol. 14(24), pages 1-17, December.

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