License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.29
URN: urn:nbn:de:0030-drops-189243
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18924/
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Feng, Zixin ; Zhao, Qunshan ; Heppenstall, Alison

Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow (Short Paper)

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LIPIcs-GIScience-2023-29.pdf (4 MB)

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.

BibTeX - Entry

@InProceedings{feng_et_al:LIPIcs.GIScience.2023.29,
  author =	{Feng, Zixin and Zhao, Qunshan and Heppenstall, Alison},
  title =	{{Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{29:1--29:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18924},
  URN =		{urn:nbn:de:0030-drops-189243},
  doi =		{10.4230/LIPIcs.GIScience.2023.29},
  annote =	{Keywords: Electric vehicles, agent-based modelling, charging demand, route choices}
}

Keywords: Electric vehicles, agent-based modelling, charging demand, route choices
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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