Optimal placement of hydrogen vehicle fueling stations using geographic information systems and multiple criteria decision-making
Bibliographic record
Abstract
Recent advances in hydrogen production and storage have supported the growth of hydrogen fuel cell vehicles as a cleaner alternative to internal combustion engines under key conditions. These vehicles offer fast refueling and reduced emissions but face production and infrastructure challenges, especially the limited availability and high cost of hydrogen fueling stations (HFSs). This study focuses on Edmonton, Canada, and aims to identify optimal locations for HFSs by integrating multiple spatial and decision-making tools. A Geographic Information System-based suitability analysis was conducted using census data, traffic volumes, and city layout. Criteria weights were determined using the Entropy Weight Method and the Criteria Importance Through Intercriteria Correlation (CRITIC) method, revealing that gas station proximity (23.3%), major road proximity (14.3%), and slope (13.9%) were the most influential factors. The P-median model was then applied to select optimal HFS locations based on different scenarios. The optimal five-station configuration achieved broad geographic balance and placed approximately 120 000 residents, around 12% of Edmonton's population, within a 5 min drive. Coverage increased to 69% with 25 stations. All selected sites were located on or near existing gas stations or commercial parcels, improving feasibility. These findings offer a defensible, data-driven foundation for phased HFS deployment and provide valuable insights for transportation planning, land use policy, and the transition to clean energy in urban areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".