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

Chengxiang Zhuge, Chunfu Shao and Xia Li
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Chengxiang Zhuge: Department of Geography, University of Cambridge, Downing Place, Cambridge CB2 3EN, UK
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

Energies, 2019, vol. 12, issue 16, 1-21

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.

Keywords: parking behaviour; charging behaviour; electric vehicles; Multinomial Logit (MNL) model; Beijing; agent-based modelling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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