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Research on Parking Recommendation Methods Considering Travelers’ Decision Behaviors and Psychological Characteristics

Author

Listed:
  • Huanmei Qin

    (Beijing Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Ning Xu

    (Beijing Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Yonghuan Zhang

    (Beijing Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Qianqian Pang

    (School of Transportation, Southeast University, Nanjing 214135, China)

  • Zhaolin Lu

    (Beijing Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

Abstract

Intelligent parking services can provide parking recommendations and reservations for travelers. They are an effective method for solving the cruising for parking problems in big cities. This research conducted a sequential parking decision behavior survey and analyzed travelers’ parking choices and reservation behaviors at different stages of the travel process. Then, a parking recommendation model was established to consider the travelers’ psychological thresholds and the attention to parking factors. The effects of different parking recommendation schemes in different situations were further explored based on parking simulations. It was concluded that travelers were more willing to accept and use the parking recommendation system. A total of 56% of travelers chose to make parking reservations during the travel process. The satisfaction proportion of the psychological threshold for the parking reservation group was higher than that for the non-parking reservation group. A dynamic parking recommendation scheme with a regulation threshold can change the recommendation strategy according to the overall utilization of parking lots. The implementation of the scheme can not only improve travelers’ parking experience, but it can also effectively balance the utilization of parking resources. It can be applied to different parking utilization situations and produce good performance. The research results provide references for the design and application of intelligent parking services in order to solve parking problems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6808-:d:1126385
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    References listed on IDEAS

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    1. Soto, Jose J. & Márquez, Luis & Macea, Luis F., 2018. "Accounting for attitudes on parking choice: An integrated choice and latent variable approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 65-77.
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    Cited by:

    1. 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.
    2. Gürcan Sarısoy & Hüseyin Onur Tezcan, 2024. "Does Parking Type Preference Behavior Differ According to Whether It Is Paid or Free? A Case Study in Istanbul, Türkiye," Sustainability, MDPI, vol. 16(17), pages 1-25, August.

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