Locating Alternative Fuel Stations for Maximizing Coverage and Ensuring Sufficient Spacing: a Case Study of CNG Truck Fueling
While a number of optimization models have been proposed for siting refueling/recharging stations for alternative fuel/electric vehicles, many of these approaches require detailed origin–destination (OD) data of refueling trips that are often very costly or challenging to obtain. This paper introduces two new arc-based coverage models for locating alternative fuel stations for regions where OD data are unavailable or unsuitable. Station spacing parameters are proposed to reduce redundant coverage while helping fill in regional coverage gaps. The first model is more suitable for early stages of planning, while the second addresses coverage overlap issues during later stages when a denser station network is formed. Both models can handle cases with a set of pre-existing stations. The new models are applied to planning a network of compressed natural gas (CNG) fueling stations for heavy-duty CNG-powered trucking in the Southwest USA. The models are generalizable to other regions, fuels, and vehicle types when applicable OD data are lacking.
KeywordsAlternative fuel station location Annual average daily traffic (AADT) coverage CNG truck fueling Location models
This work was funded by the Arizona Board of Regents’ Research Innovation Fund with matching funds from Arizona State University and the University of Arizona.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that there is no conflict of interest.
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