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Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China

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  • Chengxiang Zhuge

    (Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UK
    Tyndall Centre for Climate Change Research, University of East Anglia, Norwich NR4 7TJ, UK
    Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Chunfu Shao

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuancun, Xizhimenwai, Beijing 100044, China)

  • Xia Li

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

Abstract

An empirical study of the parking behaviour of Conventional Vehicles (CVs), Battery Electric Vehicles (BEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) was carried out with the data collected in a paper-based questionnaire survey in Beijing, China. The study investigated the factors that might influence the parking behaviour, with a focus on the maximum acceptable time of walking from parking lot to trip destination, parking fee, the availability of charging posts, the state of charge of EVs and the range anxiety of BEVs. Several Multinomial Logit (MNL) models were developed to explore the relationships between individual attributes and parking choices. The results suggest that (1) the maximum acceptable walking time generally increases with the rise in the amount of saving for parking fee; (2) the availability of charging posts does not influence the maximum acceptable walking time when PHEVs and BEVs have sufficient charge, but the percentage of people willing to walk longer than eight minutes increases from around 35% to 46% when PHEVs are in a low stage of charge; (3) more than half of BEV drivers want the driving range of their vehicles to be one and a half times the driving distance before they depart, given the distance is 50 km. Based on the empirical findings above, a conceptual framework was proposed to explicitly simulate the parking behaviour of both CVs and EVs using agent-based modelling.

Suggested Citation

  • Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China," Energies, MDPI, vol. 12(16), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3073-:d:256337
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    References listed on IDEAS

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    3. Zhuge, Chengxiang & Wei, Binru & Shao, Chunfu & Shan, Yuli & Dong, Chunjiao, 2020. "The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Beijing," Energy Policy, Elsevier, vol. 139(C).
    4. 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.

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