A multi-scale framework for fuel station location: From highways to street intersections. (September 2019)

• Record Type: Journal Article
• Authors: Zhao, Qunshan; Kelley, Scott B.; Xiao, Fan; Kuby, Michael J.
• Is Part Of: Transportation research. Volume 74(2019)
• Page Start: 48
• Page End: 64
• Publication Date: 2019-09
• Subjects: Alternative fuel -- Infrastructure -- Hydrogen -- Path -- Flow -- Connecticut; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2019.07.018
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlights: Planning an effective network of fuel stations is a multi-scale location problem. The method integrates operations research and GIS network analysis techniques. Optimization models maximize coverage of local, metropolitan, and intercity trips. GIS tool assesses accessibility of local street intersections from/to freeways. Scenario planning approach converges on robust set of stations for Hartford CT, USA. Abstract: Electric drive vehicles (plug-in electric vehicle or hydrogen fuel cell vehicles) have been promoted by governments to foster a more sustainable transportation future. Wider adoption of these vehicles, however, depends on the availability of a convenient and reliable refueling/recharging infrastructure. This paper introduces a path-based, multi-scale, scenario-planning modeling framework for locating a system of alternative-fuel stations. The approach builds on (1) the Flow Refueling Location Model (FRLM), which assumes that drivers stop along their origin-destination routes to refuel, and checks explicitly whether round trips can be completed without running out of fuel, and (2) the Freeway Traffic Capture Method (FTCM), which assesses the degree to which drivers can conveniently reach sites on the local street network near freeway intersections. This paper extends the FTCM to handle cases involving clusters of nearby freeway intersections, which is a limitation of its previous specification. Then, the cluster-based FTCM (CFTCM) is integrated with the FRLM and the DFRLM (FRLM with Deviations) to better conduct detailed geographic optimization of this multi-scale location planning problem. The main contribution of this research is the introduction of a framework that combines multi-scale planning methods to more effectively inform the early development stage of hydrogen refueling infrastructure planning. The proposed multi-scale modeling framework is applied to the Hartford, Connecticut region, which is one of the next areas targeted for fuel-cell vehicle (FCV) market and infrastructure expansion in the United States. This method is generalizable to other regions or other types of fast-fueling alternative fuel vehicles.