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Optimal Deployment of Alternative Fueling Stations on Transportation Networks Considering Deviation Paths

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Abstract

The lack of sufficient public fueling stations for Alternative Fuel Vehicles (AFVs) has greatly hindered their adoption. In this paper, we describe a novel Alternative Fueling Station (AFS) location model by considering the behaviors of AFV users who are willing to deviate slightly from their most preferred routes to ensure that their AFVs with limited travel ranges can be refueled en route to their destinations. The model considers multiple deviation paths between each of the origin–destination (O-D) pairs. It relaxes the commonly adopted assumption that travelers only take a shortest path between any O-D pairs. The model provides the most cost-effective deployment strategy of siting AFSs that are needed on the network to satisfy AFV demand between all O-D pairs. We examine the model on two test networks, the Sioux Falls network and a 25-node network, and draw insights into the numerical tradeoffs between station deployment, vehicle ranges, and route deviations. The results show that deviation paths can greatly reduce the cost of establishing AFSs on networks without compromising user convenience much. In addition, an “elbow point” rule is used to identify the most cost-effective AFV travel range in terms of the total cost of building AFSs.

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Notes

  1. The Yen’s algorithm can be broken down into two parts: determining the first shortest path and determining all other (K-1)-shortest paths. Let set A hold K shortest paths, whereas set B temporarily hold shortest paths through iterations. To determine A 1, a shortest path from the source (origin) to the sink (destination) node, any efficient shortest path algorithm can be used. To find the A k, where k ranges from 2 to K, the algorithm assumes that all paths from A 1 to A K-1 have previously been found. At the K th iteration, there are two processes: firstly finding all the deviations A K i , where i ranges from 1 to the second last node (say the (N-1)st node) on the A K-1 (as the last node is the sink node itself), and Secondly choosing a shortest path between i and the sink node that has never chosen before, and adding it to set B, which is a set temporarily holding shortest paths. By the end of the iteration, set B contains |N − 1| shortest paths and the K th shortest path is the path in set B with lowest cost. This path is then removed from B and inserted into A, that is, A k. We refer interested readers to (Yen 1971) for details.

  2. At this point, the marginal gain drops (Ketchen and Shook 1996), which in the study refers to the marginal number of stations needed.

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Authors and Affiliations

  1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, 29634, USA

    Yongxi Huang & Shengyin Li

  2. H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Zhen Sean Qian

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  1. Yongxi Huang
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  2. Shengyin Li
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  3. Zhen Sean Qian
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Correspondence to Yongxi Huang.

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Huang, Y., Li, S. & Qian, Z.S. Optimal Deployment of Alternative Fueling Stations on Transportation Networks Considering Deviation Paths. Netw Spat Econ 15, 183–204 (2015). https://doi.org/10.1007/s11067-014-9275-1

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