Networks and Spatial Economics

, Volume 15, Issue 1, pp 183–204 | Cite as

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

  • Yongxi Huang
  • Shengyin Li
  • Zhen Sean Qian


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.


Alternative fueling location Deviation path Set-covering problem Optimization 


  1. Adler J, Mirchandani P, Xue G, Xia M (2014) The electric vehicle shortest-walk problem with battery exchanges. Netw Spat Econ:1–19Google Scholar
  2. Berman O, Larson RC, Fouska N (1992) Optimal location of discretionary service facilities. Transp Sci 26(3):201–211CrossRefGoogle Scholar
  3. Berman O, Bertsimas D, Larson RC (1995) Locating discretionary service facilities, ii: maximizing market size, minimizing inconvenience. Oper Res 43(4):623–632CrossRefGoogle Scholar
  4. Daskin MS (1995) Network and discrete location: Models, algorithms and applications. Wiley, New YorkCrossRefGoogle Scholar
  5. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271CrossRefGoogle Scholar
  6. Ducruet C, Beauguitte L (2013) Spatial science and network science: Review and outcomes of a complex relationship. Netw Spat Econ:1–20Google Scholar
  7. Fourer R, Gay D, Kernighan B (2003) Ampl: A modeling language for mathematical programming Book, Whole 2nd edn. Duxbury Press, BostonGoogle Scholar
  8. Frade I, Ribeiro A, Gonçalves G, Antunes A (2011) Optimal location of charging stations for electric vehicles in a neighborhood in lisbon, portugal. Transport Res Rec 2252(1):91–98CrossRefGoogle Scholar
  9. Frick M, Axhausen KW, Carle G, Wokaun A (2007) Optimization of the distribution of compressed natural gas (cng) refueling stations: swiss case studies. Transport Res: Part D: Transport Environ 12(1):10–22CrossRefGoogle Scholar
  10. He F, Yin Y, Wang J, Yang Y (2013) Sustainability SI: Optimal prices of electricity at public charging stations for plug-in electric vehicles. Netw Spat Econ:1–24Google Scholar
  11. Hodgson MJ (1990) A flow-capturing location-allocation model. Geogr Anal 22(3):270–279CrossRefGoogle Scholar
  12. Hodgson MJ, Berman O (1997) A billboard location model. Geogr Environ Model 1:25–45Google Scholar
  13. Hodgson MJ, Rosing KE, Zhang J (1996) Locating vehicle inspection stations to protect a transportation network. Geogr Anal 28(4):299–314CrossRefGoogle Scholar
  14. Hoffman W, Pavley R (1959) A method for the solution of the nth best path problem. J ACM 6(4):506–514CrossRefGoogle Scholar
  15. Ip A, Fong S, Liu E Optimization for allocating bev recharging stations in urban areas by using hierarchical clustering. In: Advanced Information Management and Service (IMS), 2010 6th International Conference on, 2010. IEEE, pp 460–465Google Scholar
  16. Ketchen DJ, Shook CL (1996) The application of cluster analysis in strategic management research: an analysis and critique. Strateg Manag J 17(6):441–458CrossRefGoogle Scholar
  17. Kim JG, Kuby M (2012) The deviation-flow refueling location model for optimizing a network of refueling stations. Int J Hydrogen Energ 37(6):5406–5420CrossRefGoogle Scholar
  18. Kuby M, Lim S (2005) The flow-refueling location problem for alternative-fuel vehicles. Socio Econ Plan Sci 39(2):125–145CrossRefGoogle Scholar
  19. Kuby M, Lines L, Schultz R, Xie Z, Kim J-G, Lim S (2009) Optimization of hydrogen stations in florida using the flow-refueling location model. Int J Hydrogen Energ 34(15):6045–6064CrossRefGoogle Scholar
  20. LeBlanc LJ, Morlok EK, Pierskalla WP (1975) An efficient approach to solving the road network equilibrium traffic assignment problem. Transport Res 9:309–318CrossRefGoogle Scholar
  21. Lim S, Kuby M (2010) Heuristic algorithms for siting alternative-fuel stations using the flow-refueling location model. Euro J Oper Res 204(1):51–61CrossRefGoogle Scholar
  22. Martins EV, Pascoal MB (2003) A new implementation of yen’s ranking loopless paths algorithm. Q J Belg Fr Ital Oper Res Soc 1(2):121–133Google Scholar
  23. MirHassani SA, Ebrazi R (2013) A flexible reformulation of the refueling station location problem. Trans Sci 47(4):617–628CrossRefGoogle Scholar
  24. Nicholas M, Handy S, Sperling D (2004) Using geographic information systems to evaluate siting and networks of hydrogen stations. Transport Res Rec 1880(1):126–134CrossRefGoogle Scholar
  25. Qian Z, Zhang HM (2013) A hybrid route choice model for dynamic traffic assignment. Netw Spat Econ 13(2):183–203CrossRefGoogle Scholar
  26. Simchi-Levi D, Berman O (1988) A heuristic algorithm for the traveling salesman location problem on networks. Oper Res 36(3):478–484CrossRefGoogle Scholar
  27. Stephens-Romero SD, Brown TM, Kang JE, Recker WW, Samuelsen GS (2010) Systematic planning to optimize investments in hydrogen infrastructure deployment. Int J Hydrogen Energ 35(10):4652–4667CrossRefGoogle Scholar
  28. Wang Y-W (2007) An optimal location choice model for recreation-oriented scooter recharge stations. Transport Res: Part D: Transport Environ 12(3):231–237CrossRefGoogle Scholar
  29. Wang Y-W (2008) Locating battery exchange stations to serve tourism transport: A note. Transport Res: Part D: Transport Environ 13(3):193–197CrossRefGoogle Scholar
  30. Wang YW, Lin CC (2009) Locating road-vehicle refueling stations. Transport Res: Part E: Logist Transport Rev 45(5):821–829CrossRefGoogle Scholar
  31. Wen M, Laporte G, Madsen OBG, Norrelund AV, Olsen A (2013) Locating replenishment stations for electric vehicles: application to danish traffic data. J Oper Res Soc 65:1555–1561CrossRefGoogle Scholar
  32. Yen JY (1971) Finding the k shortest loopless paths in a network. Manage Sci 17(11):712–716CrossRefGoogle Scholar
  33. Zeng W, Castillo I, Hodgson MJ (2010) A Generalized Model for Locating Facilities on a Network with Flow-Based Demand. Networks and Spatial Economics 10(4):579–611Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Glenn Department of Civil EngineeringClemson UniversityClemsonUSA
  2. 2.H. John Heinz III CollegeCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations