Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Sign In or Register Submit

PDF
PUBLISHED
27.08.2024
DOI
https://doi.org/10.7307/ptt.v36i4.494
ISSUE
2024 (Vol 36), Issue 4
LICENSE
Copyright (c) 2024 Tianyang GAO, Dawei HU, Gang CHEN, Steven CHIEN, Bingshan MA
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Optimising Electric Flex-Route Feeder Transit Service with Dynamic Wireless Charging Technology

Authors:Tianyang GAO, Dawei HU, Gang CHEN, Steven CHIEN, Bingshan MA

Abstract

The emergence of battery electric buses (BEBs) can alleviate environmental problems caused by tailpipe emissions in transit system. However, the high cost of on-board batteries and range anxiety hinder its further development. Recently, the advent of dynamic wireless power transfer technology (DWPT) has become a potential solution to promote the development of BEBs. Hence, this study focuses on the application of DWPT in flex-route transit system. A mixed integer non-linear model is proposed to simultaneously optimise the bus routing and the selection of corresponding bus types considering the constraints of passengers’ travel time, battery size and bus capacity. The objective is to minimise both transit agency cost and passengers’ travel time cost. A tangible hybrid variable neighbourhood search (HVNS) consisting of simulated annealing (SA) and variable neighbourhood search (VNS) is developed to solve the proposed model efficiently. Compared with GAMS (DICOPT solver) and VNS, the proposed algorithm can considerably improve computational efficiency. The results suggest that the proposed model can effectively determine the BEBs’ routing and bus type for flex-route transit system powered by DWPT through a case study in Xi’an China. A comparative analysis shows the proposed model takes 12.97% less total cost than the alternative model with terminal charging technology (TCT).

Keywords:flex-route feeder transit, electric bus, dynamic wireless charging, opportunity charging, hybrid variable neighbourhood search

References

  1. [1] I.E. Agency, CO2 Emissions from Fuel Combustion 2020. International Energy Agency, 2020. https://www.iea.org/reports/greenhouse-gas-emissions-from-energy-overview/data-explorer
  2. [2] Lajunen A, Lipman T. Lifecycle cost assessment and carbon dioxide emissions of diesel, natural gas, hybrid electric, fuel cell hybrid and electric transit buses. Energy. 2016;106:329-342. DOI: 10.1016/j.energy.2016.03.075.
  3. [3] Bi Z, et al. Plug-in vs. wireless charging: life cycle energy and greenhouse gas emission analysis of an electric bus system. Applied Energy. 2015;146:11-19. DOI: 10.1016/j.apenergy.2015.02.031.
  4. [4] Bi Z, et al. A review of wireless power transfer for electric vehicles: Prospects to enhance sustainable mobility. Applied Energy. 2016;179(oct.1):413-425. DOI: 10.1016/j.apenergy.2016.07.003.
  5. [5] He Z. Battery electric bus selection based on entropy weight method and road operation test: Using Nanjing bus company as an example. Mathematical Problems in Engineering. 2022;2022:1696578. DOI: 10.1155/2022/1696578.
  6. [6] Jeong S, Jang Y, Kum D. Economic analysis of the dynamic charging electric vehicle. IEEE Transactions on Power Electronics. 2015;30(11):6368-6377. DOI: 10.1109/TPEL.2015.2424712.
  7. [7] Jang Y, Jeong S, Ko Y. System optimization of the on-line electric vehicle operating in a closed environment. Computers & Industrial Engineering. 2015;80:222-235. DOI: 10.1016/j.cie.2014.12.004.
  8. [8] Liu Z, Song Z, Yi H. Optimal deployment of dynamic wireless charging facilities for an electric bus system. Transportation Research Record. 2017;2647(1):128-139. DOI: 10.3141/2647-12.
  9. [9] Jang Y, Suh E, Kim J. System architecture and mathematical models of electric transit bus system utilizing wireless power transfer technology. IEEE Systems Journal. 2016;10(2):495-506. DOI: 10.1109/JSYST.2014.2369485.
  10. [10] Liu Z, Song Z. Robust planning of dynamic wireless charging infrastructure for battery electric buses. Transportation Research Part C: Emerging Technologies. 2017;83:77-103. DOI: 10.1016/j.trc.2017.07.013.
  11. [11] Mouhrim N, El Hilali Alaoui A, Boukachour J. Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network. International Journal of Sustainable Transportation. 2018;13(6):419-432. DOI: 10.1080/15568318.2018.1481242.
  12. [12] Alwesabi Y, et al. Robust strategic planning of dynamic wireless charging infrastructure for electric buses. Applied Energy. 2021;307:118243. DOI: 10.1016/j.apenergy.2021.118243.
  13. [13] Quadrifoglio L, Dessouky M, Ordónez F. A simulation study of demand responsive transit system design. Transportation Research Part A: Policy & Practice. 2008;42(4):718-737. DOI: 10.1016/j.tra.2008.01.018.
  14. [14] Quadrifoglio L, Dessouky M, Ordónez F. Mobility allowance shuttle transit (MAST) services: MIP formulation and strengthening with logic constraints. European Journal of Operational Research. 2008;185(2):481-494. DOI: 10.1016/j.ejor.2006.12.030.
  15. [15] Quadrifoglio L, Dessouky M, Palmer K. An insertion heuristic for scheduling Mobility Allowance Shuttle Transit (MAST) services. Journal of Scheduling. 2007;10(1):25-40. DOI: 10.1007/s10951-006-0324-6.
  16. [16] Kim M, Schonfeld P. Conventional, flexible, and variable-type bus services. Journal of Transportation Engineering. 2012;138(3):263-273. DOI: 10.1061/(ASCE)TE.1943-5436.0000326.
  17. [17] Kim M, Schonfeld P. Integration of conventional and flexible bus services with timed transfers. Transportation Research Part B: Methodological. 2014;68b(oct.):76-97. DOI: 10.1016/j.trb.2014.05.017.
  18. [18] Kim M, Schonfeld P. Maximizing net benefits for conventional and flexible bus services. Transportation Research Part A: Policy & Practice. 2015;80A(OCT.): 116-133. DOI: 10.1016/j.tra.2015.07.016.
  19. [19] Yu Y, Machemehl R, Xie C. Demand-responsive transit circulator service network design. Transportation Research Part E: Logistics and Transportation Review. 2015;76:160-175. DOI: 10.1016/j.tre.2015.02.009.
  20. [20] Pan S, et al. Designing a flexible feeder transit system serving irregularly shaped and gated communities: Determining service area and feeder route planning. Journal of Urban Planning & Development. 2015;141(3):04014028.04014021-04014028.04014029. DOI: 10.1061/(ASCE)UP.1943-5444.0000224.
  21. [21] Pei M, Lin P, Du J, Li X. Operational design for a real-time flexible transit system considering passenger demand and willingness to pay. IEEE Access. 2019;7:180305-180315. DOI: 10.1109/access.2019.2949246.
  22. [22] Wang Y, et al. Optimizing centralized dispatching of flexible feeder transit considering transfer coordination with regular public transit. Mathematical Problems in Engineering. 2021;2021:6160321. DOI: 10.1155/2021/6160321.
  23. [23] Cheng C, Wang T, Wang W, Ding J. Designing customised bus routes for urban commuters with the existence of multimodal network - A bi-level programming approach. Promet–Trafic&Transportation. 2022;34(3):487-498. DOI: 10.7307/ptt.v34i3.3980
  24. [24] Wu M, et al. Joint optimization of timetabling, vehicle scheduling, and ride-matching in a flexible multi-type shuttle bus system. Transportation Research Part C: Emerging Technologies. 2022;139:103657. DOI: 10.1016/j.trc.2022.103657.
  25. [25] Masmoudi M, et al. The dial-a-ride problem with electric vehicles and battery swapping stations. Transportation Research Part E: Logistics and Transportation Review. 2018;118:392–420. DOI: 10.1016/j.tre.2018.08.005.
  26. [26] Bongiovanni C, Kaspi M, Geroliminis N. The electric autonomous dial-a-ride problem. Transportation Research Part B: Methodological. 2019;122:436-456. DOI: 10.1016/j.trb.2019.03.004.
  27. [27] Li X, et al. Electric demand-responsive transit routing with opportunity charging strategy. Transportation Research Part D: Transport and Environment. 2022;110:103427. DOI: 10.1016/j.trd.2022.103427.
  28. [28] Molenbruch Y, Braekers K, Eisenhandler O, Kaspi M. The electric dial-a-ride problem on a fixed circuit. Transportation Science. 2023;APR. DOI: 10.1287/trsc.2023.1208.
  29. [29] Ko, Y, Jang, Y. The optimal system design of the online electric vehicle utilizing wireless power transmission technology. IEEE Transactions on intelligent transportation systems. 2013;14(3):1255-1265. DOI: 10.1109/TITS.2013.2259159.
  30. [30] He Y, Song Z, Liu Z. Fast-charging station deployment for battery electric bus systems considering electricity demand charges. Sustainable Cities & Society. 2019;48:101530. DOI: 10.1016/j.scs.2019.101530.
  31. [31] Chen G, Hu D, Chien S. Optimizing battery-electric-feeder service and wireless charging locations with nested genetic algorithm. IEEE Access. 2020;8:67166-67178. DOI: 10.1109/ACCESS.2020.2985168.
  32. [32] Yıldırım Ş, Yıldız B. Electric bus fleet composition and scheduling. Transportation Research Part C: Emerging Technologies. 2021;129:103197. DOI: 10.1016/j.trc.2021.103197.
  33. [33] Luo X, Fan W. Joint design of electric bus transit service and wireless charging facilities. Transportation Research Part E: Logistics & Transportation Review. 2023;174:103114. DOI: 10.1016/j.tre.2023.103114.
  34. [34] Kirchler D, Wolfler Calvo R. A granular tabu search algorithm for the dial-a-ride problem. Transportation Research Part B: Methodological. 2013;56:120-135. DOI: 10.1016/j.trb.2013.07.014.
  35. [35] Muelas S, Latorre A, Pena J. A variable neighborhood search algorithm for the optimization of a dial-a-ride problem in a large city. Expert Systems with Applications. 2013;40(14):5516-5531. DOI: 10.1016/j.eswa.2013.04.015.
  36. [36] Masmoudi M, Braekers K, Masmoudi M, Dammak A. A hybrid genetic algorithm for the heterogeneous dial-aride problem. Computers & Operations Research. 2017;81(MAY):1-13. DOI: 10.1016/j.cor.2016.12.008.
  37. [37] Felipe Á, Ortuno M, Righini G, Tirado G. A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics & Transportation Review. 2014;71:111-128. DOI: 10.1016/j.tre.2014.09.003.
  38. [38] Goeke D, Schneider M. Routing a mixed fleet of electric and conventional vehicles. European Journal of Operational Research. 2015;245(1):81-99. DOI: 10.1016/j.ejor.2015.01.049.
  39. [39] Hiermann G, Puchinger J, Ropke S, Hartl R. The electric fleet size and mix vehicle routing problem with time windows and recharging stations. European Journal of Operational Research. 2016;252(3):995-1018. DOI: 10.1016/j.ejor.2016.01.038.
  40. [40] Hof J, Schneider M, Goeke D. Solving the battery swap station location-routing problem with capacitated electric vehicles using an AVNS algorithm for vehicle-routing problems with intermediate stops. Transportation Research Part B: Methodological. 2017;97(MAR):102-112. DOI: 10.1016/j.trb.2016.11.009.
  41. [41] Pelletier S, Jabali O, Laporte G. The electric vehicle routing problem with energy consumption uncertainty. Transportation Research Part B: Methodological. 2019;126:225-255. DOI: 10.1016/j.trb.2019.06.006.
  42. [42] Bac U, Erdem M. Optimization of electric vehicle recharge schedule and routing problem with time windows and partial recharge: A comparative study for an urban logistics fleet. Sustainable Cities and Society. 2021;70:102883. DOI: 10.1016/j.scs.2021.102883.
  43. [43] Ma B, et al. The vehicle routing problem with speed optimization for shared autonomous electric vehicles service. Computers & Industrial Engineering. 2021;161:107614. DOI: 10.1016/j.cie.2021.107614.
  44. [44] Ma B, et al. Time-dependent vehicle routing problem with departure time and speed optimization for shared autonomous electric vehicle service. Applied Mathematical Modelling. 2023;113:333-357. DOI: 10.1016/j.apm.2022.09.020.
  45. [45] Bai Z, et al. A robust approach to integrated wireless charging infrastructure design and bus fleet size optimization. Computers & Industrial Engineering. 2022;168:108046. DOI: 10.1016/j.cie.2022.108046.
  46. [46] Lu X, et al. Flexible feeder transit route design to enhance service accessibility in urban area. Journal of Advanced Transportation. 2016;50(4):507-521. DOI: 10.1002/atr.1357.
  47. [47] Xiao M, Chien S, Hu D. Optimizing coordinated transfer with probabilistic vehicle arrivals and passengers’ walking time. Journal of Advanced Transportation. 2016;50(8):2306-2322. DOI: 10.1002/atr.1460.
  48. [48] Winter K, Cats O, Correia G, Van Arem B. Designing an automated demand-responsive transport system: Fleet size and performance analysis for a campus–train station service. Transportation Research Record. 2016;2542(1):75-83. DOI:10.3141/2542-09.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Bibliometric Analysis of Traffic Accident Prediction Studies from 2003 to 2023: Trends, Patterns and Future DirectionsResearch on the Efficiency of Major Airports in China’s Six Major Airport Cluster: Based on Common Frontier Super-Efficiency DEA and Malmquist MethodA Study on the Impact of Overtaking Lane-Changing Behaviour in Expressway Interchange Weaving AreasVehicle Trajectory Prediction Based on GAT and LSTM Networks in Urban Environments

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal