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
30.10.2023
DOI
https://doi.org/10.7307/ptt.v35i5.219
ISSUE
2023 (Vol 35), Issue 5
LICENSE
Copyright (c) 2024 Zhenyang Qiu, Xiaowei Hu, Shuai Song, Yu Wang
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Optimising Electric Bus Departure Interval Considering Stochastic Traffic Conditions

Authors:Zhenyang Qiu, Xiaowei Hu, Shuai Song, Yu Wang

Abstract

Electric buses (EBs) have attracted more and more attention in recent years because of their energy-saving and pollution-free characteristics. However, very few studies have considered the impact of stochastic traffic conditions on their operations. This paper focuses on the departure interval optimisation of EBs which is a critical problem in the operations. We consider the stochastic traffic conditions in the operations and establish a departure interval optimisation model. The objective function aims at minimising passenger travel costs and enterprise operation costs, including waiting time costs, congestion costs, energy consumption costs and operational fixed costs. To solve this problem, a genetic algorithm (GA) based on fitness adjustment crossover and mutation rate is proposed. Based on the Harbin bus dataset, we find that improved GA performance is 4.481% higher, and it can solve the models more accurately and efficiently. Compared with the current situation, the optimisation model reduces passenger travel costs by 20.2% and helps improve passenger travel quality. Under stochastic traffic conditions, total cost change is small, but passenger travel costs increase significantly. This indicates the high impact degree of random traffic conditions on passenger travel. In addition, a sensitivity analysis is conducted to provide suggestions for improving the EBs operation and management.

Keywords:electric buses, public transit, departure interval, stochastic traffic conditions, genetic algorithm

References

  1. [1] Patil R, Rahegaonkar A, Patange A, Nalavade S. Designing an optimized schedule of transit electric bus charging: A municipal level case study. Materials Today: Proceedings. 2022;56:2653-2658. DOI: 10.1016/j.matpr.2021.09.220.
  2. [2] Jiang M, Zhang Y, Zhang Y. Electric bus scheduling considering limited charging facility capacity for large-scale operation. Journal of Transportation Engineering, Part A: Systems. 2022;148(9):04022058. DOI: 10.1061/JTEPBS.0000706.
  3. [3] Mahmoud M, Garnett R, Ferguson M, Kanaroglou P. Electric buses: A review of alternative powertrains. Renewable and Sustainable Energy Reviews. 2016;62:673-684. DOI: 10.1016/j.rser.2016.05.019.
  4. [4] Li JQ. Transit bus scheduling with limited energy. Transportation Science. 2014;48(4):521-539. DOI: 10.1287/trsc.2013.0468.
  5. [5] Li JQ. Battery-electric transit bus developments and operations: A review. International Journal of Sustainable Transportation. 2016;10(3):157-169. DOI: 10.1080/15568318.2013.872737.
  6. [6] Bloomberg New Energy Finance. Electric vehicle outlook 2020. https://www.sustainable-bus.com/news/electric-vehicle-outlook-2020-bnef-electric-buses/ [Accessed 24 Aug. 2021].
  7. [7] Zuo W, Hu X, Li C. The prediction model of electric vehicle charging demand in cold regions considering environmental adaptability. CICTP 2019, 6-8 July 2019, Nanjing, China. 2019. p. 4733-4745. DOI: 10.1061/9780784482292.408.
  8. [8] Häll CH, Ceder A, Ekström J, Quttineh NH. Adjustments of public transit operations planning process for the use of electric buses. Journal of Intelligent Transportation Systems. 2019;23(3):216-230. DOI: 10.1080/15472450.2018.1488131.
  9. [9] Perumal SSG, Lusby RM, Larsen J. Electric bus planning & scheduling: A review of related problems and methodologies. European Journal of Operational Research. 2022;301(2):395-413. DOI: 10.1016/j.ejor.2021.10.058.
  10. [10] Ibarra-Rojas OJ, Delgado F, Giesen R, Muñoz JC. Planning, operation, and control of bus transport systems: A literature review. Transportation Research Part B: Methodological. 2015;77:38-75. DOI: 10.1016/j.trb.2015.03.002.
  11. [11] Xiong J, Bai C, Guan W, Xu Y. Mixed optimization on vehicle scheduling and recharge scheduling of plug-in electric buses with consideration of partial recharge. Journal of Transportation Engineering, Part A: Systems. 2022;148(2):04021111. DOI: 10.1061/JTEPBS.0000643.
  12. [12] Shui X, Zuo X, Chen C, Smith AE. A clonal selection algorithm for urban bus vehicle scheduling. Applied Soft Computing. 2015;36:36-44. DOI: 10.1016/j.asoc.2015.07.001.
  13. [13] Alwesabi Y, Wang Y, Avalos R, Liu Z. Electric bus scheduling under single depot dynamic wireless charging infrastructure planning. Energy. 2020;213:118855. DOI: 10.1016/j.energy.2020.118855.
  14. [14] Janovec M, Koháni M. Exact approach to the electric bus fleet scheduling. Transportation Research Procedia. 2019;40:1380-1387. DOI: 10.1016/j.trpro.2019.07.191.
  15. [15] Tang X, Lin X, He F. Robust scheduling strategies of electric buses under stochastic traffic conditions. Transportation Research Part C: Emerging Technologies. 2019;105:163-182. DOI: 10.1016/j.trc.2019.05.032.
  16. [16] Hadas Y, Shnaiderman M. Public-transit frequency setting using minimum-cost approach with stochastic demand and travel time. Transportation Research Part B: Methodological. 2012;46(8):1068-1084. DOI: 10.1016/j.trb.2012.02.010.
  17. [17] Bie Y, Ji J, Wang X, Qu X. Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption. Computer‐Aided Civil and Infrastructure Engineering. 2021;36(12):1530-1548. DOI: 10.1111/mice.12684.
  18. [18] Shen Y, Xu J, Li J. A probabilistic model for vehicle scheduling based on stochastic trip times. Transportation Research Part B: Methodological. 2016;85:19-31. DOI: 10.1016/j.trb.2015.12.016.
  19. [19] Pepin AS, Desaulniers G, Hertz A, Huisman D. A comparison of five heuristics for the multiple depot vehicle scheduling problem. Journal of Scheduling. 2009;12:17-30. DOI: 10.1007/s10951-008-0072-x.
  20. [20] Teng J, Chen T, Fan WD. Integrated approach to vehicle scheduling and bus timetabling for an electric bus line. Journal of Transportation Engineering, Part A: Systems. 2020;146(2):04019073. DOI: 10.1061/JTEPBS.0000306.
  21. [21] Perumal SSG, et al. Solution approaches for integrated vehicle and crew scheduling with electric buses. Computers & Operations Research. 2021;132:105268. DOI: 10.1016/j.cor.2021.105268.
  22. [22] Potthoff D, Huisman D, Desaulniers G. Column generation with dynamic duty selection for railway crew rescheduling. Transportation Science. 2010;44(4):493-505. DOI: 10.1287/trsc.1100.0322.
  23. [23] Huisman D, Freling R, Wagelmans APM. Multiple-depot integrated vehicle and crew scheduling. Transportation Science. 2005;39(4):491-502. DOI: 10.1287/trsc.1040.0104.
  24. [24] Jiang R, et al. Predicting bus travel time with hybrid incomplete data–A deep learning approach. Promet – Traffic&Transportation. 2022;34(5):673-685. DOI: 10.7307/ptt.v34i5.4052.
  25. [25] Gkiotsalitis K, Wu Z, Cats O. A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options. Transportation Research Part C: Emerging Technologies. 2019;98:14-36. DOI: 10.1016/j.trc.2018.11.007.
  26. [26] Li Y, Xu W, He S. Expected value model for optimizing the multiple bus headways. Applied Mathematics and Computation. 2013;219(11):5849-5861. DOI: 10.1016/j.amc.2012.11.098.
  27. [27] Tang CT, Li X. Optimizing mixed transit fleet replacement with multi-type battery electric buses. Journal of Transportation Systems Engineering and Information Technology. 2021;21(2):151-157. DOI: 10.16097/j.cnki.1009-6744.2021.02.022.
  28. [28] Pei Y, Shen X, Zhou K. Average waiting time model for passengers transfering from high-speed railway to bus. Journal of Traffic and Transportation Engineering. 2013;13(6):76-82.
  29. [29] Wang D, Ye Y, Zhu W, Qi H. Estimation of crowding costs in bus based on contingent valuation method. Journal of Jilin University (Engineering and Technology Edition). 2016;46(01):57-62. DOI: 10.13229/j.cnki.jdxbgxb201601009.
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
Vehicle Trajectory Prediction Based on GAT and LSTM Networks in Urban EnvironmentsA Review of Expert Systems Integration in Signal Plan OptimisationEstimating Vehicle Turn-In Rate of Expressway Rest Areas via ETC Gantry Data – An ADPC-GMM ApproachWireless Channel Propagation Model for Inland Waterway Bridge Scenario

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