On the way or around the corner? Observed refueling choices of alternative-fuel drivers in Southern California

Kelley, Scott; Kuby, Michael

doi:10.1016/j.jtrangeo.2013.08.008

Cited by 47 publications

(34 citation statements)

References 33 publications

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“…The CNG deviation decay curve actually resembles a modified distance decay curve with a ''frictionless zone'' before distance decay occurs. While not a popular functional form for spatial interaction modeling, the zone of indifference was included in some early human geography books (Fellmann et al, 1997) and is also an available option in ESRI's (2013) Spatial Statistics toolbox. A reverse Scurve could also describe this functional form (Kim and Kuby, 2012).…”

Section: Discussionmentioning

confidence: 99%

Spatial refueling patterns of alternative-fuel and gasoline vehicle drivers in Los Angeles

Kuby

Kelley

Schoenemann

2013

Transportation Research Part D: Transport and Environment

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Section: Discussionmentioning

confidence: 99%

Spatial refueling patterns of alternative-fuel and gasoline vehicle drivers in Los Angeles

Kuby

Kelley

Schoenemann

2013

Transportation Research Part D: Transport and Environment

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“…Lines et al (2008) also finds stated evidence for this willingness to deviate, noting that early adopters would go a mile out of their way in order to access a hydrogen refueling station. Kelley and Kuby (2013) find that AFV drivers tolerate up to about a six-minute deviation when accessing CNG stations in Southern California before exhibiting a sharp decay. None of these studies consider the deviation required to leave and return to limited access roads before continuing their trip, which is of specific interest to this study.…”

Section: Introductionmentioning

confidence: 92%

“…For this study, the deviation tolerance threshold is considered to be six minutes, which is the point at which deviation frequency began to decay in the sampled population of 259 CNG drivers in Kelley and Kuby (2013). An illustration of a freeway intersection, the aforementioned metrics, and an example of a comparison of travel routes is shown in Figure 1.…”

Section: The Freeway Traffic Capture Metricmentioning

confidence: 99%

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

Kelley

2016

International Journal of Geographical Information Science

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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

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“…Nicholas et al [29] studied whether the refueling of AFVs (Alternative Fuel Vehicles) could be informed by refueling experiences from the traditional petrochemical refueling network. Kuby [30] and Kelly [31] systematically studied the differences in refueling behavior between liquid fossil fuel vehicles and alternative fuel vehicles and proposed a decision model of AFV refueling according to actual refueling data for Alternative Fuel Vehicles in Southern California. As seen from the above references, the existing refueling decision models have mainly studied the problem of how to plan the refueling route to save fuel costs, optimizing the economy of refueling by using mathematical modeling and other methods, which has been very useful for guiding people to save more in fuel costs.…”

Section: Motivationmentioning

confidence: 99%

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

Yang

Peng

et al. 2018

IJGI

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Abstract:Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel.

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On the way or around the corner? Observed refueling choices of alternative-fuel drivers in Southern California

Cited by 47 publications

References 33 publications

Spatial refueling patterns of alternative-fuel and gasoline vehicle drivers in Los Angeles

Spatial refueling patterns of alternative-fuel and gasoline vehicle drivers in Los Angeles

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

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

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