A Review of Sustainable Location–Allocation Models for City Logistics Using High-Speed Rail: Case Study of the Ottawa–Quebec Corridor and Greater Montreal Area

This review examines sustainable location–allocation models for city logistics leveraging high-speed rail (HSR), with a case focus on the Ottawa–Quebec corridor and Greater Montreal Area (GMA). We address three research questions: How do metaheuristics and Graph Neural Network (GNN-RL) optimize HSR-based logistics hubs? What are the comparative strengths of p-median, hub-and-spoke, and maximal coverage models in mid-density corridors? How can insights from global HSR logistics networks inform North American planning? A systematic bibliometric analysis (2014–2024) of Scopus and Web of Science datasets, processed via VOSviewer, identifies trends in algorithmic applications (e.g., genetic algorithms, neural networks). Case studies from China, Japan, and the EU are contrasted with GMA’s nascent HSR logistics potential. Findings highlight trade-offs between computational efficiency and solution robustness, offering policy recommendations for sustainable freight integration.