City-Scale Optimization of Public Electric Vehicle Charging Infrastructure: Spatio-Temporal Demand Forecasting, Graph Learning and Multi-Objective Siting

Electric vehicle (EV) adoption is accelerating rapidly, increasing pressure on public charging infrastructure, urban energy systems, and network-expansion planning. However, many existing studies examine charging demand forecasting, station network characteristics, or infrastructure siting as separate problems, limiting their ability to translate observed charging behaviour into coordinated planning decisions. To address this gap, this study proposes a multi-domain machine learning framework that integrates spatio-temporal charging demand, station-level infrastructure characteristics, land-use information, and graph-based network relationships for EV charging prediction and infrastructure siting. The analysis uses 8,544,695 public charging transactions recorded at 8553 stations across Beijing during January and July 2025. Charging sessions are aggregated into an hourly station panel and modelled using seasonal-naïve and historical mean baselines, ridge regression, gradient-boosted trees, graph-augmented gradient boosting, and a gated recurrent unit network. Model reliability is assessed through temporal validation, feature ablation, spatial holdout testing, cross-season transfer analysis, explainability, and non-parametric statistical comparison. The results show that graph-augmented gradient boosting achieved the strongest RMSE and variance-explained performance, with an RMSE of 53.80 kWh and R2 = 0.734, while the gated recurrent unit produced the lowest MAE of 23.82 kWh. Graph neighbour features yielded only a marginal and statistically non-significant forecasting improvement, indicating that the station network is structurally informative but predictively redundant once temporal history is available. For infrastructure expansion, NSGA-II achieved the highest Pareto front hypervolume and identified a knee-point solution of 156 additional chargers, reducing unmet demand by 43.4%. These findings demonstrate that integrated forecasting, graph analysis, and multi-objective siting can provide more defensible and operationally relevant evidence for city-scale EV charging planning.