Skip to main content

Advertisement

SpringerLink
Log in

Mixed-Vehicular Aggregated Transportation Network Design Considering En-route Recharge Service Provision for Electric Vehicles

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

This paper addresses the transportation network design problem (NDP) wherein the distance limit and en-route recharge of electric vehicles are taken into account. Specifically, in this work, the network design problem aims to select the optimal planning policy from a set of infrastructure design scenarios considering both road expansions and charging station allocations under a specified construction budget. The user-equilibrium mixed-vehicular traffic assignment problem with en-route recharge (MVTAP-ER) is formulated into a novel convex optimization model and extended to a newly developed bi-level program of the aggregated NDP integrating recharge facility allocation (NDP-RFA). In the algorithmic framework, a convex optimization technique and a tailored GA are adopted for, respectively, solving the subproblem MVTAP-ER and the primal problem NDP-RFA. Systematic experiments are conducted to test the efficacy of the proposed approaches. The results highlight the impacts of distance limits and budget levels on the project selection and evaluation, and the benefits of considering both road improvement policy and recharge service provision as compared to accounting for the latter only. The results also report that the two design objectives, to respectively minimize the total system travel time and vehicle miles travelled, are conflicting for certain scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Gardner L M, Duell M, and Waller S T, A framework for evaluating the role of electric vehicles in transportation network infrastructure under travel demand variability, Transportation Research Part A: Policy and Practice, 2013, 49: 76–90.

    Google Scholar 

  2. Tamor M A, Gearhart C, and Soto C, A statistical approach to estimating acceptance of electric vehicles and electrification of personal transportation, Transportation Research Part C: Emerging Technologies, 2013, 26: 125–134.

    Article  Google Scholar 

  3. Burns L D, Sustainable mobility: A vision of our transport future, Nature, 2013, 497(7448): 181–182.

    Article  Google Scholar 

  4. Mock P, Schmid S, and Friedrich H E, Market Prospects of Electric Passenger Vehicles, Hybrid and Electric Vehicles, Power Sources, Models, Sustainability, Infrastructure and the Market, Elsevier Verlag, 2010.

    Google Scholar 

  5. Chiou S W, Bilevel programming for the continuous transport network design problem, Transportation Research Part B: Methodological, 2005, 39(4): 361–383.

    Article  Google Scholar 

  6. Zhang H and Gao Z, Bilevel programming model and solution method for mixed transportation network design problem, Journal of Systems Science and Complexity, 2009, 22(3): 446–459.

    Article  MathSciNet  MATH  Google Scholar 

  7. Yang H and Bell M G, Models and algorithms for road network design: A review and some new developments, Transport Reviews, 1998, 18(3): 257–278.

    Article  Google Scholar 

  8. Farahani R Z, Miandoabchi E, Szeto W Y, et al., A review of urban transportation network design problems, European Journal of Operational Research, 2013, 229(2): 281–302.

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen A, Zhou Z, Chootinan P, et al., Transport network design problem under uncertainty: A review and new developments, Transport Reviews, 2011, 31(6): 743–768.

    Article  Google Scholar 

  10. Luathep P, Sumalee A, Lam W H, et al., Global optimization method for mixed transportation network design problem: A mixed-integer linear programming approach, Transportation Research Part B: Methodological, 2011, 45(5): 808–827.

    Article  Google Scholar 

  11. Wang D Z and Lo H K, Global optimum of the linearized network design problem with equilibrium flows, Transportation Research Part B: Methodological, 2010, 44(4): 482–492.

    Article  Google Scholar 

  12. Duthie J and Waller S T, Incorporating environmental justice measures into equilibrium-based network design, Transportation Research Record: Journal of the Transportation Research Board, 2008, 2089: 58–65.

    Article  Google Scholar 

  13. Xie C, Bicriterion discrete equilibrium network design problem, Networks, 2014, 63(4): 286–305.

    Article  MathSciNet  MATH  Google Scholar 

  14. Sharma S and Mathew T V, Multiobjective network design for emission and travel-time trade-off for a sustainable large urban transportation network, Environment and Planning B: Planning and Design, 2011, 38(3): 520–538.

    Article  Google Scholar 

  15. Ferguson E M, Duthie J, and Waller S T, Comparing delay minimization and emissions minimization in the network design problem, Computer-Aided Civil and Infrastructure Engineering, 2012, 27(4): 288–302.

    Article  Google Scholar 

  16. Haijun H, Shouyang W, and Bell M G H, A bi-level formulation and quasi-Newton algorithm for stochastic equilibrium network design problem with elastic demand, Journal of Systems Science and Complexity, 2001, 14(1): 34–50.

    MathSciNet  MATH  Google Scholar 

  17. Ukkusuri S V and Patil G, Multi-period transportation network design under demand uncertainty, Transportation Research Part B: Methodological, 2009, 43(6): 625–642.

    Article  Google Scholar 

  18. Karoonsoontawong A and Waller S T, Robust dynamic continuous network design problem, Transportation Research Record: Journal of the Transportation Research Board, 2007, 2029: 58–71.

    Article  Google Scholar 

  19. Chen T, Kockelman K, and Khan M, Locating electric vehicle charging stations: Parking-based assignment method for Seattle, Washington, Transportation Research Record: Journal of the Transportation Research Board, 2013, 2385: 28–36.

    Article  Google Scholar 

  20. Riemann R, Wang D Z, and Busch F, Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium, Transportation Research Part C: Emerging Technologies, 2015, 58: 1–12.

    Article  Google Scholar 

  21. He F, Wu D, Yin Y, et al., Optimal deployment of public charging stations for plug-in hybrid electric vehicles, Transportation Research Part B: Methodological, 2013, 47: 87–101.

    Article  Google Scholar 

  22. He F, Yin Y, and Zhou J, Deploying public charging stations for electric vehicles on urban road networks, Transportation Research Part C: Emerging Technologies, 2015, 60: 227–240.

    Article  Google Scholar 

  23. Szeto W Y and Lo H K, Time-dependent transport network improvement and tolling strategies, Transportation Research Part A: Policy and Practice, 2008, 42(2): 376–391.

    Google Scholar 

  24. Fontaine P and Minner S, Benders decomposition for discrete-continuous linear bilevel problems with application to traffic network design, Transportation Research Part B: Methodological, 2014, 70: 163–172.

    Article  Google Scholar 

  25. Mathew T V and Sharma S, Capacity expansion problem for large urban transportation networks, Journal of Transportation Engineering, 2009, 135(7): 406–415.

    Article  Google Scholar 

  26. Zhang X, Wang H, and Wang W, Bi-level programming model and algorithms for stochastic network with elastic demand, Transport, 2015, 30(1): 117–128.

    Article  Google Scholar 

  27. Poorzahedy H and Abulghasemi F, Application of ant system to network design problem, Transportation, 2008, 32(3): 251–273.

    Article  Google Scholar 

  28. Gallo M, DAcierno L, and Montella B, A meta-heuristic approach for solving the urban network design problem, European Journal of Operational Research, 2010, 201(1): 144–157.

    Article  MATH  Google Scholar 

  29. Friesz T L, Cho H J, Mehta N J, et al., A simulated annealing approach to the network design problem with variational inequality constraints, Transportation Science, 1992, 26(1): 18–26.

    Article  MATH  Google Scholar 

  30. Drezner Z and Salhi S, Using hybrid metaheuristics for the oneway and twoway network design problem, Naval Research Logistics, 2002, 49(5): 449–463.

    Article  MathSciNet  MATH  Google Scholar 

  31. Bunce L, Harris M, and Burgess M, Charge up then charge out? Drivers perceptions and experiences of electric vehicles in the UK, Transportation Research Part A: Policy and Practice, 2014, 59: 278–287.

    Google Scholar 

  32. Artmeier A, Haselmayr J, Leucker M, et al., The shortest path problem revisited: Optimal routing for electric vehicles, KI, 2010, 6359: 309–316.

    Google Scholar 

  33. Wardrop J G, Road paper, Some theoretical aspects of road traffic research, Proceedings of the Institution of Civil Engineers, 1952, 1(3): 325–362.

    Google Scholar 

  34. Ziliaskopoulos A K, A linear programming model for the single destination system optimum dynamic traffic assignment problem, Transportation Science, 2000, 34(1): 37–49.

    Article  MATH  Google Scholar 

  35. Unnikrishnan A and Waller S T, User equilibrium with recourse, Networks and Spatial Economics, 2009, 9(4): 575–593.

    Article  MathSciNet  MATH  Google Scholar 

  36. Ukkusuri S V, Han L, and Doan K, Dynamic user equilibrium with a path based cell transmission model for general traffic networks, Transportation Research Part B: Methodological, 2012, 46(10): 1657–1684.

    Article  Google Scholar 

  37. Larsson T and Patriksson M, Side constrained traffic equilibrium models analysis, computation and applications, Transportation Research Part B: Methodological, 1999, 33(4): 233–264.

    Google Scholar 

  38. Jiang N, Xie C, and Waller S T, Path-constrained traffic assignment: Model and algorithm, Transportation Research Record: Journal of the Transportation Research Board, 2012, 2283: 25–33.

    Article  Google Scholar 

  39. Jiang N, Xie C, Duthie J C, et al., A network equilibrium analysis on destination, route and parking choices with mixed gasoline and electric vehicular flows, EURO Journal on Transportation and Logistics, 2014, 3(1): 55–92.

    Article  Google Scholar 

  40. Agrawal S, Zheng H, Peeta S, et al., Routing aspects of electric vehicle drivers and their effects on network performance, Transportation Research Part D: Transport and Environment, 2016, 46: 246–266.

    Article  Google Scholar 

  41. Wang T G, Xie C, Xie J, et al., Path-constrained traffic assignment: A trip chain analysis under range anxiety, Transportation Research Part C: Emerging Technologies, 2016, 68: 447–461.

    Article  Google Scholar 

  42. Xie C and Jiang N, Relay requirement and traffic assignment of electric vehicles, Computer-Aided Civil and Infrastructure Engineering, 2016, 31: 580–598.

    Article  Google Scholar 

  43. Dong J, Liu C, and Lin Z, Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data, Transportation Research Part C: Emerging Technologies, 2014, 38: 44–45.

    Article  Google Scholar 

  44. Guo Z, Deride J, and Fan Y, Infrastructure planning for fast charging stations in a competitive market, Transportation Research Part C: Emerging Technologies, 2016, 68: 215–227.

    Article  Google Scholar 

  45. Dafermos S C, The traffic assignment problem for multiclass-user transportation networks, Transportation Science, 1927, 6(1): 73–87.

    Article  Google Scholar 

  46. Musavi F and Eberle W, Overview of wireless power transfer technologies for electric vehicle battery charging, IET Power Electronics, 2014, 7(1): 60–66.

    Article  Google Scholar 

  47. Sheffi Y, Urban Transportation Network, Equilibrium analysis with Mathematical Programming Methods, Inc. Upper Saddle River, Pretince Hall, 1985.

    Google Scholar 

  48. Manual H C, Highway Capacity Manual,Transportation Research Board, Washington, D.C., 2000.

    Google Scholar 

  49. Dumitrescu I and Boland N, Improved preprocessing, labeling and scaling algorithms for the weight-constrained shortest path problem, Networks, 2003, 42(3): 135–153.

    MATH  Google Scholar 

  50. Karoonsoontawong A and Waller S T, Dynamic continuous network design problem: Linear bilevel programming and metaheuristic approaches, Transportation Research Record: Journal of the Transportation Research Board, 2006, 1964: 104–117.

    Google Scholar 

  51. Bar-Gera H, Transportation Network Test Problems, https://doi.org/www.bgu.ac.il/bargera/tntp/, Accessed February 29, 2013.

  52. Chen Z, He F, and Yin Y, Optimal deployment of charging lanes for electric vehicles in transportation networks, Transportation Research Part B: Methodological, 2016, 91: 344–365.

    Article  Google Scholar 

  53. Zhang X, Rey D, Waller S T, et al., Traffic assignment problem considering en-route recharge modes and time of plug-in electric vehicles, Proceedings of the 96th Annual Meeting of the Transportation Research Board, Washington, D.C., 2017.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil and Environmental Engineering, University of New South Wales, Kensington, Australia

    Xiang Zhang & S. Travis Waller

Authors
  1. Xiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Travis Waller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang Zhang.

Additional information

This research was supported by Research Centre for Integrated Transport Innovation, UNSW.

This paper was recommended for publication by Editor HUANG Haijun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Waller, S.T. Mixed-Vehicular Aggregated Transportation Network Design Considering En-route Recharge Service Provision for Electric Vehicles. J Syst Sci Complex 31, 1329–1349 (2018). https://doi.org/10.1007/s11424-018-7165-1

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-018-7165-1

Keywords

Search

Navigation