Abstract
For bus carriers, it is the most basic and important problem to create the bus scheduling timetable based on bus fleet configuration and passenger flow demand. Considering different technical and economic properties, vehicle capacities and limited available number of heterogeneous buses, as well as the time-space characteristics of passenger flow demand, this paper focuses on creating the bus timetables and sizing the buses simultaneously. A bi-objective optimization model is formulated, in which the first objective is to minimum the total operation cost, and the second objective is to maximum the passenger volume. The proposed model is a nonlinear integer programming, thus a genetic algorithm with self-crossover operation is designed to solve it. Finally, a case study in which the model is applied to a real-world case of a bus line in the city of Beijing, China, is presented.
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References
Yan, S.Y., Chi, C.-J., Tang, C.H.: Inter-city bus routing and timetable setting under stochastic demands. Transp. Res. Part A 40, 572–586 (2006)
Yan, Y.D., Meng, Q., Wang, S.A., Guo, X.C.: Robust optimization model of schedule design for a fixed bus route. Transp. Res. Part C 25, 113–121 (2012)
Vissat, L.L., Clark, A., Gilmore, S.: Finding optimal timetables for Edinburgh bus routes. Electron. Notes Theoret. Comput. Sci. 310(310), 179–199 (2015)
Wong, S.C., Tong, C.O.: A stochastic transit assignment model using a dynamic schedule-based network. Transp. Res. Part B 33, 107–121 (1999)
Ceder, A., Golany, B., Tal, O.: Creating bus timetables with maximal synchronization. Transp. Res. Part A 35, 913–928 (2001)
Yan, S.Y., Chen, H.L.: A scheduling model and a solution algorithm for inter-city bus carriers. Transp. Res. Part A 36, 805–825 (2002)
Wu, Y.H., Yang, H., Tang, J.F., Yu, Y.: Multi-objective re-synchronizing of bus timetable: model, complexity and solution. Transp. Res. Part C 67, 149–168 (2016)
Ceder, A., Hassold, S., Dunlop, C., Chen, I.: Improving urban public transport service using new timetabling strategies with different vehicle sizes. Int. J. Urban Sci. 17(2), 239–258 (2013)
Sun, D., Xu, Y., Peng, Z.R.: Timetable optimization for single bus line based on hybrid vehicle size model. J. Traffic Transp. Eng. 2(3), 179–186 (2015)
Hurdle, V.F.: Minimum cost schedules for a public transportation route. Transp. Sci. 7(2), 109–137 (1973)
Chriqui, C., Robillard, P.: Common bus lines. Transp. Sci. 9(2), 115–121 (1975)
Niu, H.M., Zhou, X.S.: Optimizing urban rail timetable under time-dependent demand and oversaturated conditions. Transp. Res. Part C 36, 212–230 (2013)
Nguyen, S., Pallottino, A., Malucelli, F.: A modeling framework for passenger assignment on a transport network with timetables. Transp. Sci. 35(3), 238–249 (2001)
Cominetti, R., Correa, J.: Common-lines and passenger assignment in congested. Transp. Sci. 35(3), 250–267 (2001)
Cepeda, M., Cominetti, R., Florian, M.: A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria. Transp. Res. Part B: Methodol. 40(6), 437–459 (2006)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Oxford (1975)
Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4(2), 65–85 (1994)
Jones, G., Willett, P., Glen, R.C., Leach, A.R., Taylor, R.: Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267(3), 727–748 (1997)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
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Yu, H., Ma, H., Du, H., Li, X., Xiao, R., Du, Y. (2018). Bus Scheduling Timetable Optimization Based on Hybrid Bus Sizes. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_29
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DOI: https://doi.org/10.1007/978-3-319-66939-7_29
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