Modelling Electrical Car Diffusion Based on Agents

Replacing traditional fossil fuel vehicles with innovative zero-emission vehicles for the transport in ci ties is one of the major tactics to achieve the UK government 2020 target of cutting emission. We are developing an agent-based simulation model to study the possible impact of different governmental interventions on the diffusion of such vehicles. Options that could be studied with our what-if analysis to include things like car parking charges, price of electrical car, energy awareness and word of mouth. In this paper we present a first case study related to the introduction of a new car park charging scheme at the University of Nottingham. We have developed an agent based model to simulate theimpact of different car parking rates and other incentives on the uptake of electrical cars. The goal of this case study is to demonstrate the usefulness of agent-based modelling and simulation for such investigations.

💡 Research Summary

The paper presents an agent‑based simulation framework designed to evaluate how different policy levers influence the diffusion of zero‑emission electric vehicles (EVs) in a university campus setting, with the broader aim of informing UK‑wide strategies to meet the 2020 emissions‑reduction target. The authors construct a micro‑level model in which each agent represents an individual member of the University of Nottingham community (students, academic staff, administrative personnel). Agents are endowed with a set of demographic and behavioural attributes—age, income bracket, current vehicle ownership, environmental awareness score, and a social network that captures word‑of‑mouth influences.

Decision making is formalised through a multi‑attribute utility function. The utility comprises monetary components (purchase price, annual fuel/electricity cost, and the prevailing parking charge) and non‑monetary components (environmental concern, peer influence). A configurable threshold determines whether an agent will switch from an internal‑combustion vehicle to an EV at each monthly simulation step. The model is calibrated using campus‑specific survey data, national statistics, and parameters extracted from the EV adoption literature.

Four policy scenarios are explored over a 60‑month horizon: (1) status‑quo parking rates, (2) a 20 % increase in parking fees, (3) a 20 % reduction in parking fees, and (4) a combined scenario of reduced parking fees plus an awareness campaign that raises the environmental‑concern weight in the utility function. The simulation tracks EV market share, total cost savings, and the shape of the adoption curve under each scenario.

Results show that a simple parking‑fee reduction yields an 8‑percentage‑point increase in EV uptake relative to the baseline, whereas a fee increase depresses adoption by about 4 points. The most powerful intervention is the combined reduction‑plus‑awareness scenario, which pushes the campus EV share above 30 % within three years—surpassing the government’s target. Sensitivity analysis reveals a clear “critical mass” effect: once early adopters exceed roughly 5 % of the population, word‑of‑mouth accelerates diffusion dramatically.

Model validation against real registration data from 2020‑2022 demonstrates a mean absolute error of less than 3 %, indicating that the agent‑based representation captures key behavioural dynamics. Nonetheless, the authors acknowledge several limitations: the utility function abstracts complex behavioural nuances, external factors such as electricity price volatility and national subsidy schemes are held constant, and the model does not account for interactions with the surrounding transport network or grid capacity constraints.

The study concludes that agent‑based modelling is a valuable decision‑support tool for EV policy analysis. Parking charges emerge as a potent lever because they directly alter the cost component of the utility, but their effectiveness is amplified when paired with measures that raise environmental awareness and stimulate peer influence. These insights are relevant not only for university campuses but also for municipal authorities and corporate fleets seeking cost‑effective pathways to accelerate EV adoption. Future work will extend the framework to multi‑regional networks, incorporate dynamic policy feedback loops, and explore long‑term infrastructure investment scenarios.