An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space

Sedar Olmez, Jason Thompson, Ellie Marfleet, Keiran Suchak, Alison Heppenstall, Ed Manley, Annabel Whipp and Rajith Vidanaarachchi
Additional contact information
Sedar Olmez: School of Geography, University of Leeds, Seminary St, Woodhouse, Leeds LS2 9JT, UK
Jason Thompson: Transport, Health and Urban Design Research Laboratory, The University of Melbourne, Grattan Street, Parkville, VIC 3010, Australia
Ellie Marfleet: School of Geography, University of Leeds, Seminary St, Woodhouse, Leeds LS2 9JT, UK
Keiran Suchak: School of Geography, University of Leeds, Seminary St, Woodhouse, Leeds LS2 9JT, UK
Alison Heppenstall: The Alan Turing Institute, 2QR, John Dodson House, 96 Euston Rd, London NW1 2DB, UK
Ed Manley: School of Geography, University of Leeds, Seminary St, Woodhouse, Leeds LS2 9JT, UK
Annabel Whipp: School of Geography, University of Leeds, Seminary St, Woodhouse, Leeds LS2 9JT, UK
Rajith Vidanaarachchi: Transport, Health and Urban Design Research Laboratory, The University of Melbourne, Grattan Street, Parkville, VIC 3010, Australia

Energies, 2022, vol. 15, issue 11, 1-24

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 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.

Keywords: agent-based model; electric vehicles; traffic simulation; energy intake; urban environment; fuel costs; public policy (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: 2022
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