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Published VersionCC BY V4.0, Open Access
Published VersionCC BY V4.0, Open
Journal article
An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space
Energies , Vol.15(11), pp.1-24
2022
Abstract
By 2020, over 100 countries had expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to the proven environmental and health implications of internal combustion engine vehicles (ICEVs), as evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in energy consumption; thus, simulating various levels of individual behaviour and enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that (1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour); (2) vehicles that travel for shorter distances while abiding by speed limit rules are more energy efficient compared to those that speed and travel for longer; and (3) on average, for tested vehicles, EV/PHEVs were 233.13 pound cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.
Details
- Title
- An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space
- Authors
- Sedar Olmez (Corresponding Author) - The Alan Turing InstituteJason Thompson (Author) - The University of MelbourneEllie Marfleet (Author) - University of LeedsKeiran Suchak (Author) - University of LeedsAlison Heppenstall (Author) - The Alan Turing InstituteEd Manley (Author) - The Alan Turing InstituteAnnabel Whipp (Author) - University of LeedsRajith Vidanaarachchi (Author) - The University of Melbourne
- Publication details
- Energies , Vol.15(11), pp.1-24
- Publisher
- MDPI AG
- Date published
- 2022
- DOI
- 10.3390/en15114031
- ISSN
- 1996-1073
- Copyright note
- © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
- Data Availability
- Open source code and data access: (1) the datasets for both PHEV and ICEV can be accessed at the following source [73]; (2) the Urban Traffic Simulator Agent-Based Model can be accessed at the following link: https://www.comses.net/codebases/32e7be8c-b05c-46b2-9b5f-73c4d273ca59/releases/1.1.0/ (accessed on 20 May 2022); (3) the Energy Calculation Extension can be found at the following link https://codeocean.com/capsule/9598578/tree (accessed on 25 May 2022).
- Grant note
- This project has received funding from the Economic and Social Research Council, grant number: ES/P000401/1; the Economic and Social Research Council, The Alan Turing Institute, grant number: ES/R007918/1, UK Prevention Research Partnership (UKPRP): MR/S037578/2, Medical Research Council: MC_UU_00022/5 and The Scottish Government Chief Scientist Office: SPHSU20.
- Organisation Unit
- Centre for Human Factors and Systems Science
- Language
- English
- Record Identifier
- 991184299802621
- Output Type
- Journal article
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