The current public transportation systems predominantly rely on rigid schedules and service patterns, leading to suboptimal resource allocation that impacts both passengers and transit operators. This inefficiency results in the wastage of resources and dissatisfaction among users. The unsatisfactory passenger experience significantly contributes to the declining ridership, thereby diminishing revenue for transit operators. To specifically address these challenges encountered by Lhasa's public transportation system, we propose a multi-objective model for bus departure timetables. The model aims to synchronize the costs of passenger waiting time and bus operation costs concurrently, accounting for diverse constraints such as actual travel times, operational bus numbers, bus capacity limits, and arrival time distributions. In this research, we establish a multi-objective optimization model with the primary goal of maximizing passenger satisfaction while concurrently optimizing the revenue of the transit company. Implemented in Lhasa, China, we use the Non-Dominated Sorting Genetic Algorithm-Ⅱ to derive Pareto fronts relevant for analysis. The research findings demonstrate a reduction in the frequency of departures by one bus within a one-hour timeframe. Additionally, a substantial 37% decrease is observed in both the count of buses not arriving at stations and the number of passengers waiting at these stations compared to previous timetables. These results suggest promising potential for significant benefits to both the transit company and passengers within the public transportation system.
Citation: Gang Cheng, Yijie He. Enhancing passenger comfort and operator efficiency through multi-objective bus timetable optimization[J]. Electronic Research Archive, 2024, 32(1): 565-583. doi: 10.3934/era.2024028
The current public transportation systems predominantly rely on rigid schedules and service patterns, leading to suboptimal resource allocation that impacts both passengers and transit operators. This inefficiency results in the wastage of resources and dissatisfaction among users. The unsatisfactory passenger experience significantly contributes to the declining ridership, thereby diminishing revenue for transit operators. To specifically address these challenges encountered by Lhasa's public transportation system, we propose a multi-objective model for bus departure timetables. The model aims to synchronize the costs of passenger waiting time and bus operation costs concurrently, accounting for diverse constraints such as actual travel times, operational bus numbers, bus capacity limits, and arrival time distributions. In this research, we establish a multi-objective optimization model with the primary goal of maximizing passenger satisfaction while concurrently optimizing the revenue of the transit company. Implemented in Lhasa, China, we use the Non-Dominated Sorting Genetic Algorithm-Ⅱ to derive Pareto fronts relevant for analysis. The research findings demonstrate a reduction in the frequency of departures by one bus within a one-hour timeframe. Additionally, a substantial 37% decrease is observed in both the count of buses not arriving at stations and the number of passengers waiting at these stations compared to previous timetables. These results suggest promising potential for significant benefits to both the transit company and passengers within the public transportation system.
| [1] |
A. Ceder, B. Golany, O. Tal, Creating bus timetables with maximal synchronization, Transp. Res. Part A: Policy Pract., 35 (2001), 913–928. https://doi.org/10.1016/S0965-8564(00)00032-X doi: 10.1016/S0965-8564(00)00032-X
|
| [2] |
V. Guihaire, J. K. Hao, Transit network design and scheduling: A global review, Transp. Res. Part A: Policy Pract., 42 (2008), 1251–1273. https://doi.org/10.1016/j.tra.2008.03.011 doi: 10.1016/j.tra.2008.03.011
|
| [3] |
G. Laporte, Scheduling issues in vehicle routing, Ann. Oper. Res., 236 (2016), 463–474. https://doi.org/10.1007/s10479-013-1423-3 doi: 10.1007/s10479-013-1423-3
|
| [4] |
P. Lin, C. He, L. Zhong, M. Pei, C. Zhou, Y. Liu, Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort, Electron. Res. Arch., 31 (2023), 2315–2336.. http://dx.doi.org/10.3934/era.2023118 doi: 10.3934/era.2023118
|
| [5] |
J. Tang, Y. Yang, W. Hao, F. Liu, Y. Wang, A data-driven timetable optimization of urban bus line based on multi-objective genetic algorithm, IEEE Trans. Intell. Transp. Syst., 22 (2020), 2417–2429. https://doi.org/10.1109/TITS.2020.3025031 doi: 10.1109/TITS.2020.3025031
|
| [6] |
M. Pei, P. Lin, R. Liu, Y. Ma, Flexible transit routing model considering passengers' willingness to pay, IET Intell. Transp. Syst., 13 (2019), 841–850. https://doi.org/10.1049/iet-its.2018.5220 doi: 10.1049/iet-its.2018.5220
|
| [7] |
G. Cheng, C. He, Analysis of bus travel characteristics and predictions of elderly passenger flow based on smart card data, Electron. Res. Arch., 30 (2022), 4256–4276. https://doi.org/10.3934/era.2022217 doi: 10.3934/era.2022217
|
| [8] |
S. Hassold, A. C. Avishai, Public transport vehicle scheduling featuring multiple vehicle types, Transp. Res. Part B: Methodol., 67 (2014), 129–143. https://doi.org/10.1016/j.trb.2014.04.009 doi: 10.1016/j.trb.2014.04.009
|
| [9] |
X. Yang, L. Liu, A multi-objective bus rapid transit energy saving dispatching optimization considering multiple types of vehicles, IEEE Access, 8 (2020), 79459–79471. https://doi.org/10.1109/ACCESS.2020.2989334 doi: 10.1109/ACCESS.2020.2989334
|
| [10] |
X. Yang, Y. Qi, Research on optimization of multi-objective regional public transportation scheduling, Algorithms, 14 (2021), 108. https://doi.org/10.3390/a14040108 doi: 10.3390/a14040108
|
| [11] |
S. B. Jha, K. J. Jitendra, K. T. Manoj, A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system, Comput. Ind. Eng., 130 (2019), 166–186. https://doi.org/10.1016/j.cie.2019.02.025 doi: 10.1016/j.cie.2019.02.025
|
| [12] |
M. Wei, S. Zhang, B. Sun, Comprehensive operating efficiency measurement of 28 Chinese airports using a two-stage DEA-Tobit method, Electron. Res. Arch., 31 (2023), 1543–1555. https://doi.org/10.3934/era.2023078 doi: 10.3934/era.2023078
|
| [13] |
M. Wei, S. Zhang, T. Liu, B. Sun, The adjusted passenger transportation efficiency of nine airports in China with consideration of the impact of high-speed rail network development: A two-step DEA-OLS method, J. Air Transp. Manage., 109 (2023), 102395. https://doi.org/10.1016/j.jairtraman.2023.102395 doi: 10.1016/j.jairtraman.2023.102395
|
| [14] |
M. H. Miandoab, G. Vahidreza, M. Davood, Developing a simultaneous scheduling of passenger and freight trains for an inter-city railway considering optimization of carbon emissions and waiting times, J. Cleaner Prod., 248 (2020), 119303. https://doi.org/10.1016/j.jclepro.2019.119303 doi: 10.1016/j.jclepro.2019.119303
|
| [15] |
H. Wang, Y. Wen, S. Wang, Facility planning and schedule design in the pandemic: Eliminating contacts at construction workplace, J. Cleaner Prod., 395 (2023), 136394. https://doi.org/10.1016/j.jclepro.2023.136394 doi: 10.1016/j.jclepro.2023.136394
|
| [16] |
H. Wang, R. Yan, S. Wang, L. Zhen, Innovative approaches to addressing the tradeoff between interpretability and accuracy in ship fuel consumption prediction, Transp. Res. Part C: Emerging Technol., 157 (2023), 104361. https://doi.org/10.1016/j.trc.2023.104361 doi: 10.1016/j.trc.2023.104361
|
| [17] |
C. Wang, Z. Ye, W. Wang, A multi-objective optimization and hybrid heuristic approach for urban bus route network design, IEEE Access, 8 (2020), 12154–12167. https://doi.org/10.1109/ACCESS.2020.2966008 doi: 10.1109/ACCESS.2020.2966008
|
| [18] |
Z. Jun, Initial boundary value problem for a inhomogeneous pseudo-parabolic equation, Electron. Res. Arch., 28 (2020), 67–90. https://doi.org/10.3934/era.2020005 doi: 10.3934/era.2020005
|
| [19] |
Y. Liu, F. Wu, C. Lyu, S. Li, J. Ye, X. Qu, Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform, Transp. Res. Part E: Logist. Transp. Rev., 161 (2022), 102694. https://doi.org/10.1016/j.tre.2022.102694 doi: 10.1016/j.tre.2022.102694
|
| [20] |
Y. Bie, R. Tang, Z. Liu, D. Ma, Mixed scheduling strategy for high frequency bus routes with common stops, IEEE Access, 8 (2020), 34442–34454. https://doi.org/10.1109/ACCESS.2020.2974740 doi: 10.1109/ACCESS.2020.2974740
|
| [21] |
Y. Bie, M. Hao, M. Guo, Optimal electric bus scheduling based on the combination of all-stop and short-turning strategies, Sustainability, 13 (2021), 1827. https://doi.org/10.3390/su13041827 doi: 10.3390/su13041827
|
| [22] | B. Hu, X. Wang, W. Chen, Study on combinational scheduling between inter-zone vehicle and regular vehicle for urban public transit, J. Wuhan Univ. Technol. (Transp. Sci. Eng.), 36 (2012), 1192–1195. https://api.semanticscholar.org/CorpusID:112197872 |
| [23] |
M. Kim, S. Paul, Conventional, flexible, and variable-type bus services, J. Transp. Eng., 138 (2012), 263–273. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000326 doi: 10.1061/(ASCE)TE.1943-5436.0000326
|
| [24] | Z. Zhou, Z. Ye, Y. Xu, Optimizing combined bus service pattern and frequencies with genetic algorithm, in 18th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. https://doi.org/10.1061/9780784481523.088 |
| [25] |
Z. Ning, S. Sun, M. C. Zhou, X. Hu, X. Wang, L. Guo, et al., Online scheduling and route planning for shared buses in urban traffic networks, IEEE Trans. Intell. Transp. Syst., 23 (2021), 3430–3444. https://doi.org/10.1109/TITS.2020.3036396 doi: 10.1109/TITS.2020.3036396
|
| [26] |
Y. Liu, L. Wang, Z. Zeng, Y. Bie, Optimal charging plan for electric bus considering time-of-day electricity tariff, J. Intell. Connected Veh., 5 (2022), 123–137. https://doi.org/10.1108/JICV-04-2022-0008 doi: 10.1108/JICV-04-2022-0008
|
| [27] |
T. Liu, A. C. Avishai, Analysis of a new public-transport-service concept: Customized bus in China, Transp. Policy, 39 (2015), 63–76. https://doi.org/10.1016/j.tranpol.2015.02.004 doi: 10.1016/j.tranpol.2015.02.004
|
| [28] |
W. Zhang, H. Zhao, M. Xu, Optimal operating strategy of short turning lines for the battery electric bus system, Commun. Transp. Res., 1 (2021), 100023. https://doi.org/10.1016/j.commtr.2021.100023 doi: 10.1016/j.commtr.2021.100023
|
| [29] |
Y. Yang, Z. Z. Yuan, J. Y. Li, Y. H. Wang, W. C. Wang, Multi-mode public transit OD prediction and scheduling model, Adv. Transp. Stud., 2018 (2018). https://doi.org/10.4399/978882552166517 doi: 10.4399/978882552166517
|
| [30] |
S. I. Wahjono, A. Marina, M. Y. K. Bachok, M. Mochklas, The Importance of MPIS on RK for further ITS implementation in Malaysia, Int. J. Adv. Appl. Sci. (IJAAS), 4 (2017), 53–60. https://doi.org/10.21833/ijaas.2017.09.007 doi: 10.21833/ijaas.2017.09.007
|
| [31] |
J. Ji, Y. Bie, Z. Zeng, L. Wang, Trip energy consumption estimation for electric buses, Commun. Transp. Res., 2 (2022), 100069. https://doi.org/10.1016/j.commtr.2022.100069 doi: 10.1016/j.commtr.2022.100069
|
| [32] |
Y. Liu, E. Yao, S. Liu, Energy consumption optimization model of multi-type bus operating organization based on time-space network, Appl. Sci., 9 (2019), 3352. https://doi.org/10.3390/app9163352 doi: 10.3390/app9163352
|
| [33] |
G. J. Zhou, D. F. Xie, X. M. Zhao, C. Lu, Collaborative optimization of vehicle and charging scheduling for a bus fleet mixed with electric and traditional buses, IEEE Access, 8 (2020), 8056–8072. https://doi.org/10.1109/ACCESS.2020.2964391 doi: 10.1109/ACCESS.2020.2964391
|
| [34] |
X. Kong, M. Li, T. Tang, K. Tian, L. Moreira-Matias, F. Xia, Shared subway shuttle bus route planning based on transport data analytics, IEEE Trans. Autom. Sci. Eng., 15 (2018), 1507–1520. https://doi.org/10.1109/TASE.2018.2865494 doi: 10.1109/TASE.2018.2865494
|
| [35] |
X. Kong, F. Xia, J. Li, M. Hou, M. Li, Y. Xiang, A shared bus profiling scheme for smart cities based on heterogeneous mobile crowdsourced data, IEEE Trans. Ind. Inf., 16 (2019), 1436–1444. https://doi.org/10.1109/TII.2019.2947063 doi: 10.1109/TII.2019.2947063
|
| [36] |
A. Jahic, M. Plenz, M. Eskander, D. Schulz, Route scheduling for centralized electric bus depots, IEEE Open J. Intell. Transp. Syst., 2 (2021), 149–159. https://doi.org/10.1109/OJITS.2021.3096115 doi: 10.1109/OJITS.2021.3096115
|
| [37] |
X. Zuo, C. Chen, W. Tan, M. Zhou, Vehicle scheduling of an urban bus line via an improved multiobjective genetic algorithm, IEEE Trans. Intell. Transp. Syst., 16 (2014), 1030–1041. https://doi: 10.1109/TITS.2014.2352599 doi: 10.1109/TITS.2014.2352599
|
| [38] |
S. Yan, C. J. Chi, C. H. Tang, Inter-city bus routing and timetable setting under stochastic demands, Transp. Res. Part A: Policy Pract., 40 (2006), 572–586. https://doi.org/10.1016/j.tra.2005.11.006 doi: 10.1016/j.tra.2005.11.006
|
| [39] |
D. J. Sun, Y. Xu, Z. Peng, Timetable optimization for single bus line based on hybrid vehicle size model, J. Traffic Transp. Eng., 2 (2015), 179–186. https://doi.org/10.1016/j.jtte.2015.03.006 doi: 10.1016/j.jtte.2015.03.006
|
| [40] | F. D. Wihartiko, A. Buono, B. P. Silalahi, Integer programming model for optimizing bus timetable using genetic algorithm, in IOP Conference Series: Materials Science and Engineering. Vol. 166. No. 1. IOP Publishing, 2017. https://doi.org/10.1088/1757-899X/166/1/012016 |
| [41] |
J. Parbo, O. A. Nielsen, C. G. Prato, User perspectives in public transport timetable optimization, Transp. Res. Part C: Emerging Technol., 48 (2014), 269–284. https://doi.org/10.1016/j.trc.2014.09.005 doi: 10.1016/j.trc.2014.09.005
|
| [42] |
K. Gkiotsalitis, F. Alesiani, Robust timetable optimization for bus lines subject to resource and regulatory constraints, Transp. Res. Part E: Logist. Transp. Rev., 128 (2019), 30–51. https://doi.org/10.1016/j.tre.2019.05.016 doi: 10.1016/j.tre.2019.05.016
|
| [43] |
H. Yan, Z. Cui, X. Chen, X. Ma, Distributed Multiagent Deep Reinforcement Learning for Multiline Dynamic Bus timetable optimization, IEEE Trans. Ind. Inf., 19 (2022), 469–479. https://doi.org/10.1109/TII.2022.3158651 doi: 10.1109/TII.2022.3158651
|
| [44] |
H. Ma, X. Li, H. Yu, Single bus line timetable optimization with big data: A case study in Beijing, Inf. Sci., 536 (2020), 53–66. https://doi.org/10.1016/j.ins.2020.03.108 doi: 10.1016/j.ins.2020.03.108
|
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Gang Cheng, Yijie He. Enhancing passenger comfort and operator efficiency through multi-objective bus timetable optimization[J]. Electronic Research Archive, 2024, 32(1): 565-583. doi: 10.3934/era.2024028
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