Machine-Learning-Based Suitability Modelling for Electric Vehicle Charging Station Development in Bosnia and Herzegovina

Planning future electric vehicle charging infrastructure requires assessment of spatial suitability, demand, network gaps, and expected accessibility benefits. This study developed a national-scale framework for Bosnia and Herzegovina integrating machine-learning (ML) suitability modelling, post-modelling prioritisation, reproducible candidate selection, and scenario-based population accessibility assessment. A dataset of 188 EVCS locations and spatially balanced pseudo-absences was analysed using 28 spatial predictors. Five classifiers were evaluated through spatial cross-validation, with XGBoost providing the most balanced performance. After feature reduction, the final model retained five predictors: road density, travel time to hotels, travel time to parking, distance to major roads, and travel time to tourist attractions. The priority index combined modelled suitability with population demand, LU/LC opportunity, and the travel-time gap to existing EVCSs. Candidate locations were derived using a deterministic settlement- and road-constrained procedure followed by network-based spacing, and scenarios with 10, 20, 40, 50, and 60 new EVCSs were evaluated. At the 10 min threshold, population coverage increased from 57.9% for the existing network to 65.4% with 10 new EVCSs, 68.7% with 20, 73.8% with 40, 75.6% with 50, and 77.0% with 60. The framework provides a reproducible basis for national EVCS investment screening while distinguishing occurrence-based suitability, strategic deployment priority, and expected accessibility gains.