An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas

The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a Silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.