Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow

Feng, Z., Zhao, Q. and Heppenstall, A. (2023) Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow. In: 12th International Conference on Geographic Information Science (GIScience 2023), Leeds, UK, 14-19 Sept 2023, 29:1-29:6. ISBN 9783959772884 (doi: 10.4230/LIPIcs.GIScience.2023.29)

Abstract

With the new policy aimed at advancing the phase-out date for the sale of new petrol and diesel cars and vans to 2030, the electric vehicle (EV) market share is expected to rise significantly in the coming years. This necessitates a deeper understanding of the driving and charging behaviours of EV drivers to accurately estimate future charging demand distribution and benefit for future infrastructure development. Traditional data-based approaches are limited in illustrating the granular spatiotemporal dynamics of individuals. Recent studies that use conventional vehicle trajectory data also have the sampling bias problem, despite their analyses being conducted at a finer resolution. Moreover, studies that use simulation approaches are often either based on limited behaviour rules for EV drivers or implemented in an artificial grid environment, showing limitations in reflecting real-world situations. To address the challenges, this work introduces an agent-based model (ABM) with complex behaviour rules for EV drivers, taking into account the drivers’ sensitivities to financial and time costs, as well as route deviation. By integrating the simulation model with the origin and destination information of drivers, this work can contribute to a better understanding of the behaviour patterns of EV drivers.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heppenstall, Professor Alison and Zhao, Dr Qunshan and Feng, Zixin
• Authors: Feng, Z., Zhao, Q., and Heppenstall, A.
• College/School: College of Social Sciences > School of Social and Political Sciences; College of Social Sciences > School of Social and Political Sciences > Urban Studies
• ISSN: 1868-8969
• ISBN: 9783959772884
• Copyright Holders: Copyright © Zixin Feng, Qunshan Zhao, and Alison Heppenstall
• First Published: First published in 12th International Conference on Geographic Information Science (GIScience 2023)
• Publisher Policy: Reproduced under a Creative Commons license

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
190698		Urban Big Data Research Centre	Nick Bailey	Economic and Social Research Council (ESRC)	ES/L011921/1	S&PS - Urban Big Data
304042		UBDC Centre Transition	Nick Bailey	Economic and Social Research Council (ESRC)	ES/S007105/1	S&PS - Administration