AFV refueling stations and the complexity of freeway intersections: the scale dependency of network representation

Abstract

As interest for alternative fuel vehicles (AFVs) in urban areas continues to build, there has been a corresponding focus on the development of methods that can locate initial refueling stations on regional transportation networks in an optimal manner. Recent studies suggest that the behavior of early adopters of AFVs is consistent with the objectives of flow-capturing station location models. One outstanding issue with the further use of these models to locate AFV stations is the relationship between the relatively coarse geographic representation of regional transportation networks used in flow-capturing models and the complexity of the sites they recommend, which are often nodes that represent freeway intersections. Since AFV refueling stations cannot be built directly at freeway intersections, a network GIS method entitled the freeway traffic capture metric (FTCM) is developed to identify locations on proximate local street networks that can effectively capture all traffic passing through a freeway intersection. The FTCM considers deviation from a shortest travel path specifically through a freeway intersection as its metric of convenience for all possible travel routes that pass through it, computing a score for each candidate site on the nearby local street network. The FTCM is tested on nearly 45,000 candidate sites within one mile of 72 freeway intersections in greater Los Angeles, California. Locations that can capture all traffic passing through the nearby freeway intersection are shown to generally be near the intersection's center, but not necessarily next to freeway entrances or exits. In total, 6.7% of possible station locations near these 72 freeway intersections are convenient for all traffic passing through them, and 22 out of the 72 freeway intersections lack such a site altogether. This indicates that there is high likelihood of inconsistency between regional modeling results and station location effectiveness if this scale dependency between coarse representations of transportation networks and the complexity of local street networks near freeway intersections is not considered when building initial AFV stations near these locations. ARTICLE HISTORY

A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel

“…The vehicle refueling strategies studied in this paper were established based on travel route planning for self-driving travel, and it is not clear whether the algorithm can adapt to refueling processes in other travel paths. This is because there may be different self-driving routes for different travel paths, and the refueling recommendation algorithm designed in this study can only adapt to a single-line path and cannot meet the refueling demands of a complex road network [29,48]. In spite of this, the generality of this algorithm on a single line is very good, and accordingly, we conclude that no matter what travel path is selected, as long as self-driving behaviors exist and the traveling route consists of a series of single lines, the vehicle refueling recommendation strategies based on this algorithm are applicable to this kind of situation.…”

A multi-scale framework for fuel station location: From highways to street intersections

“…Trip-based approaches, in particular, frequently select these nodes because of the high path volumes flowing through them (Kuby et al, 2009). To systematically identify effective station sites near major freeway intersections, Kelley (2017) developed the Freeway Traffic Capture Method (FTCM) to help address the inherent scale dependency between regional optimization models and local road networks, where stations must be built. This study found only 7% of street intersections near each of the 72 freeway intersections in Los Angeles could be conveniently reached by drivers on all possible travel paths that passed through the freeway intersection, signaling that an ad-hoc station site selection process near freeway intersections is unlikely to identify a convenient location.…”

“…The typical FRLM and DFRLM modeling approach works well for recommending generalized station locations on a simplified regional road network, but does not check whether suitably accessible nearby sites exist where stations could be built. To address this, we incorporate and extend the Freeway Traffic Capture Method (FTCM), which measures how conveniently drivers can reach street intersections surrounding freeway intersections for all possible travel routes passing through them, to identify accessible sites on the local street networks (Kelley, 2017). Using these integrated methods, we recommend initial sets of five and ten stations for the greater Hartford, Connecticut region.…”

Driver Use and Perceptions of Refueling Stations Near Freeways in a Developing Infrastructure for Alternative Fuel Vehicles

“…Increasingly, the AFV refueling station location literature has begun to specifically consider locations along transportation corridors that carry high amounts of passing traffic to support AFV adoption (Kuby and Lim 2005;Lin et al 2008;Chung and Kwon 2015). In urban areas, this means that station locations near limited-access highways and freeways are of particular interest, as they are conceivably uniquely able to meet refueling demand from long-distance trips passing by them, along with additional local residential and fleet demand (Kelley 2017). However, little is known about the refueling behavior of early AFV adopters that use publicly-available AFV refueling stations near freeways in urban environments, nor about how they currently perceive, access, and consider other AFV stations in sparse refueling networks.…”

“…These kinds of deviations and considerations required to refuel at a station near a freeway or freeway intersection are not currently accounted for in existing station location models. This is a critical shortcoming in the current applicability of models that recommend stations in these locations, because if drivers cannot easily exit the freeway, reach the station, and return to the freeway for all possible travel directions through a freeway intersection, the amount of refueling demand satisfied by a network node selected for such a station location may be inconsistent with any modeling result (Kelley 2017). Further, while the time and effort required to leave and re-enter limited-access highways to reach refueling stations near freeways in urban environments may be an important consideration for early AFV adopters, other factors are likely to influence a driver's willingness to use them.…”