Optimal placement of hydrogen vehicle fueling stations using geographic information systems and multiple criteria decision-making

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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Publication Details

Journal
Canadian Journal of Civil Engineering
Published
2025-09-05
DOI
https://doi.org/10.1139/cjce-2025-0229
Citations
1
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
2.11
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article

Optimal placement of hydrogen vehicle fueling stations using geographic information systems and multiple criteria decision-making

Stephen D. Wong, A.M.N. Sakr, Nancy Huynh, Michael Urbiztondo et al.
1 citations
Canadian Journal of Civil Engineering
Multi-Criteria Decision Making
2.11
article

Optimal placement of hydrogen vehicle fueling stations using geographic information systems and multiple criteria decision-making

Stephen D. Wong, A.M.N. Sakr, Nancy Huynh, Michael Urbiztondo, Erfan Hasanpour Zaryabi
article en
1 citations

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.

Canadian Journal of Civil EngineeringVol. 52(10)
University of Alberta (CA)
Openalex Percentile: Top 11%
Multi-Criteria Decision Making
2.11
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