GIS-Based Multi-Criteria Optimization of EV Charging Stations Integrated into Public Lighting Infrastructure

The rapid growth of electric vehicle (EV) adoption requires the scalable and cost-effective deployment of publicly accessible charging infrastructure, where cost-effectiveness is understood in terms of infrastructure reuse rather than explicit economic optimisation. Integrating slow AC charging units into existing public lighting networks represents a promising infrastructure reuse strategy, though spatial feasibility, electrical constraints, and regulatory requirements must be addressed. This study proposes an integrated GIS–MCDA–MILP framework for the optimal allocation of EV charging stations within public lighting systems. GIS-based spatial analysis identifies feasible poles based on parking accessibility and demand indicators, while MCDA ranks candidate locations and a MILP model determines optimal deployment under capacity constraints and phased rollout scenarios. The framework also incorporates AFIR-based policy benchmarking to assess compliance under current and future EV adoption levels. A real-world case study identifies 1223 feasible poles with a structural hosting capacity of 368 chargers. The results demonstrate that such integration is viable at the spatial and cabinet-capacity planning level but structurally limited, with a critical fleet growth multiplier of approximately 3.4 identified as the threshold beyond which lighting-integrated deployment alone becomes insufficient for AFIR compliance. The proposed framework advances the state of practice by coupling spatial, electrical, and regulatory analysis within a single reproducible methodology, offering a transferable decision-support tool for sustainable urban EV charging planning.