Fair assignment of urban-rural electric vehicle shared charging using queuing theory

Maybury, Lucy, Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 and Cipcigan, Liana ORCID: https://orcid.org/0000-0002-5015-3334 2025. Fair assignment of urban-rural electric vehicle shared charging using queuing theory. European Transport Studies 2 , p. 100039. 10.1016/j.ets.2025.100039

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Abstract

The widespread adoption of electric vehicles (EVs) is crucial for decarbonising transportation and achieving global net-zero goals. However, a significant challenge in this transition is ensuring equitable access to charging infrastructure, particularly when addressing the simultaneous charging needs of urban residents and rural visitors in urban areas. This is a critical aspect often overlooked in existing literature. This study formulates the problem of implementing a charging system for urban areas that can support both urban and rural users as a multi-objective integer linear programming (ILP) model. This approach uniquely achieves fairness by reducing congestion in urban charging systems to ensure sufficient charging capacity for rural residents visiting the area. Specifically, the expected mean waiting time for all users is minimised. Concurrently, the travel distance for urban residents to their assigned charging stations is also minimised, thereby ensuring sufficient charging capacity for rural residents visiting the area. An ILP solver was employed to evaluate the proposed model across various problem instances, including a detailed case study of Cardiff city, UK. Results demonstrate the significant advantages of this assignment model: for a simulated scenario with 36 charging stations in Cardiff’s urban centre, the model reduced the mean waiting time by approximately 7 min per user (from 16.6 to 9.6 min) and decreased the average travel distance for urban users by (from to ) compared to a baseline approach. Further experiments across different charging station densities consistently showed that the optimisation model reduced mean waiting times by up to 12.8 min and average travel distances by up to . This research provides a robust, data-driven framework that enables more equitable and efficient EV charging infrastructure planning, facilitating a truly inclusive transition to electric vehicles for both urban and rural communities.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Elsevier
ISSN:	2950-2985
Date of First Compliant Deposit:	31 October 2025
Date of Acceptance:	2 October 2025
Last Modified:	04 Nov 2025 11:22
URI:	https://orca.cardiff.ac.uk/id/eprint/182032

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