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Article

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

by 1,2,3, 4 and 5,*
1
Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UK
2
Tyndall Centre for Climate Change Research, University of East Anglia, Norwich NR4 7TJ, UK
3
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China
4
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuancun, Xizhimenwai, Beijing 100044, China
5
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(16), 3073; https://doi.org/10.3390/en12163073
Received: 1 July 2019 / Revised: 31 July 2019 / Accepted: 7 August 2019 / Published: 9 August 2019
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. View Full-Text
Keywords: parking behaviour; charging behaviour; electric vehicles; Multinomial Logit (MNL) model; Beijing; agent-based modelling parking behaviour; charging behaviour; electric vehicles; Multinomial Logit (MNL) model; Beijing; agent-based modelling
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MDPI and ACS Style

Zhuge, C.; Shao, C.; Li, X. Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China. Energies 2019, 12, 3073. https://doi.org/10.3390/en12163073

AMA Style

Zhuge C, Shao C, Li X. Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China. Energies. 2019; 12(16):3073. https://doi.org/10.3390/en12163073

Chicago/Turabian Style

Zhuge, Chengxiang, Chunfu Shao, and Xia Li. 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China" Energies 12, no. 16: 3073. https://doi.org/10.3390/en12163073

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