[
Alesiani, F., Moreira-Matias, L. and Faizrahnemoon, M. (2018). On learning from inaccurate and incomplete traffic flow data, IEEE Transactions on Intelligent Transportation Systems 19(11): 3698–3708.10.1109/TITS.2018.2857622
]Search in Google Scholar
[
Argote-Cabanero, J., Daganzo, C.F. and Lynn, J.W. (2015). Dynamic control of complex transit systems, Transportation Research B: Methodological 81: 146–160.10.1016/j.trb.2015.09.003
]Search in Google Scholar
[
Aslani, M., Mesgari, M.S. and Wiering, M. (2017). Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events, Transportation Research C: Emerging Technologies 85: 732–752.10.1016/j.trc.2017.09.020
]Search in Google Scholar
[
Barnett, A. (1974). On controlling randomness in transit operations, Transportation Science 8(2): 102–116.10.1287/trsc.8.2.102
]Search in Google Scholar
[
Cao, S., Shao, H. and Shao, F. (2022). Sensor location for travel time estimation based on the user equilibrium principle: Application of linear equations, International Journal of Applied Mathematics and Computer Science 32(1): 23–33, DOI: 10.34768/amcs-2022-0003.
]Ouvrir le DOISearch in Google Scholar
[
Ceder, A. (1988). Designing transit short-turn trips with the elimination of imbalanced loads, in J.R. Daduna et al. (Eds), Computer-Aided Transit Scheduling, Lecture Notes in Economics and Mathematical Systems, Vol. 308, Springer, Berlin, pp. 288–303.10.1007/978-3-642-85966-3_25
]Search in Google Scholar
[
Ceder, A. and Stern, H.I. (1981). Deficit function bus scheduling with deadheading trip insertions for fleet size reduction, Transportation Science 15(4): 338–363.10.1287/trsc.15.4.338
]Search in Google Scholar
[
Cheng, Y., Huang, Y., Pang, B. and Zhang, W. (2018). ThermalNet: A deep reinforcement learning-based combustion optimization system for coal-fired boiler, Engineering Applications of Artificial Intelligence 74: 303–311.10.1016/j.engappai.2018.07.003
]Search in Google Scholar
[
Cortés, C.E., Jara-Díaz, S. and Tirachini, A. (2011). Integrating short turning and deadheading in the optimization of transit services, Transportation Research A: Policy and Practice 45(5): 419–434.10.1016/j.tra.2011.02.002
]Search in Google Scholar
[
Daganzo, C.F. and Pilachowski, J. (2011). Reducing bunching with bus-to-bus cooperation, Transportation Research B: Methodological 45(1): 267–277.10.1016/j.trb.2010.06.005
]Search in Google Scholar
[
Estrada, M., Mensión, J., Aymamí, J.M. and Torres, L. (2016). Bus control strategies in corridors with signalized intersections, Transportation Research C: Emerging Technologies 71: 500–520.10.1016/j.trc.2016.08.013
]Search in Google Scholar
[
Fang, K., Fan, J. and Yu, B. (2022). A trip-based network travel risk: Definition and prediction, Annals of Operations Research, DOI: 10.1007/s10479-022-04630-6.
]Ouvrir le DOISearch in Google Scholar
[
Farazi, N.P., Zou, B., Ahamed, T. and Barua, L. (2021). Deep reinforcement learning in transportation research: A review, Transportation Research Interdisciplinary Perspectives 11: 100425.10.1016/j.trip.2021.100425
]Search in Google Scholar
[
Fu, L. and Yang, X. (2002). Design and implementation of bus-holding control strategies with real-time information, Transportation Research Record 1791(1): 6–12.10.3141/1791-02
]Search in Google Scholar
[
Furth, P.G. (1985). Alternating deadheading in bus route operations, Transportation Science 19(1): 13–28.10.1287/trsc.19.1.13
]Search in Google Scholar
[
Furth, P.G. (1986). Zonal route design for transit corridors, Transportation Science 20(1): 1–12.10.1287/trsc.20.1.1
]Search in Google Scholar
[
Goeke, D. and Schneider, M. (2015). Routing a mixed fleet of electric and conventional vehicles, European Journal of Operational Research 245(1): 81–99.10.1016/j.ejor.2015.01.049
]Search in Google Scholar
[
Guan, Y., Li, S.E., Duan, J., Li, J., Ren, Y., Sun, Q. and Cheng, B. (2021). Direct and indirect reinforcement learning, International Journal of Intelligent Systems 36(8): 4439–4467.10.1002/int.22466
]Search in Google Scholar
[
Hall, R., Dessouky, M. and Lu, Q. (2001). Optimal holding times at transfer stations, Computers & Industrial Engineering 40(4): 379–397.10.1016/S0360-8352(01)00039-0
]Search in Google Scholar
[
Hao, J., Liu, X., Shen, X. and Feng, N. (2019). Bilevel programming model of urban public transport network under fairness constraints, Discrete Dynamics in Nature and Society 2019: 2930502.10.1155/2019/2930502
]Search in Google Scholar
[
Hu, H., Jia, X., He, Q., Fu, S. and Liu, K. (2020). Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in Industry 4.0, Computers & Industrial Engineering 149: 106749.
]Search in Google Scholar
[
Jordan, W.C. and Turnquist, M.A. (1979). Zone scheduling of bus routes to improve service reliability, Transportation science 13(3): 242–268.10.1287/trsc.13.3.242
]Search in Google Scholar
[
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017). ImageNet classification with deep convolutional neural networks, Communications of the ACM 60(6): 84–90.10.1145/3065386
]Search in Google Scholar
[
Larrain, H., Giesen, R. and Muñoz, J.C. (2010). Choosing the right express services for bus corridor with capacity restrictions, Transportation Research Record 2197(1): 63–70.10.3141/2197-08
]Search in Google Scholar
[
Laskaris, G., Cats, O., Jenelius, E., Rinaldi, M. and Viti, F. (2019). Multiline holding based control for lines merging to a shared transit corridor, Transportmetrica B: Transport Dynamics 7(1): 1062–1095.10.1080/21680566.2018.1548312
]Search in Google Scholar
[
Li, L., Lv, Y. and Wang, F.-Y. (2016). Traffic signal timing via deep reinforcement learning, IEEE/CAA Journal of Auto-matica Sinica 3(3): 247–254.10.1109/JAS.2016.7508798
]Search in Google Scholar
[
Luo, S. (2020). Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning, Applied Soft Computing 91: 106208.10.1016/j.asoc.2020.106208
]Search in Google Scholar
[
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S. and Hassabis, D. (2015). Human-level control through deep reinforcement learning, Nature 518(7540): 529–533.10.1038/nature1423625719670
]Search in Google Scholar
[
Petit, A., Lei, C. and Ouyang, Y. (2019). Multiline bus bunching control via vehicle substitution, Transportation Research B: Methodological 126: 68–86.10.1016/j.trb.2019.05.009
]Search in Google Scholar
[
Petit, A., Ouyang, Y. and Lei, C. (2018). Dynamic bus substitution strategy for bunching intervention, Transportation Research B: Methodological 115: 1–16.10.1016/j.trb.2018.06.001
]Search in Google Scholar
[
Van Hasselt, H., Guez, A. and Silver, D. (2016). Deep reinforcement learning with double q-learning, Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, USA, pp. 2094–2100.
]Search in Google Scholar
[
Van Oort, N., Boterman, J. and van Nes, R. (2012). The impact of scheduling on service reliability: Trip-time determination and holding points in long-headway services, Public Transport 4(1): 39–56.10.1007/s12469-012-0054-4
]Search in Google Scholar
[
Vuchic, V.R. (1973). Skip-stop operation as a method for transit speed increase, Traffic Quarterly 27(2): 307–327.
]Search in Google Scholar
[
Wang, J. and Sun, L. (2020). Dynamic holding control to avoid bus bunching: A multi-agent deep reinforcement learning framework, Transportation Research C: Emerging Technologies 116: 102661.10.1016/j.trc.2020.102661
]Search in Google Scholar
[
Wang, P. and Chan, C.-Y. (2017). Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, pp. 1–6.
]Search in Google Scholar
[
Wang, T. and Tang, Y. (2021). Comprehensive evaluation of power flow and adjustment method to restore solvability based on GCRNN and DDQN, International Journal of Electrical Power & Energy Systems 133: 107160.10.1016/j.ijepes.2021.107160
]Search in Google Scholar
[
Wang, W., Chen, X., Musial, J. and Blazewicz, J. (2020). Two meta-heuristic algorithms for scheduling on unrelated machines with the late work criterion, International Journal of Applied Mathematics and Computer Science 30(3): 573–584, DOI: 10.34768/amcs-2020-0042.
]Ouvrir le DOISearch in Google Scholar
[
Xuan, Y., Argote, J. and Daganzo, C.F. (2011). Dynamic bus holding strategies for schedule reliability: Optimal linear control and performance analysis, Transportation Research B: Methodological 45(10): 1831–1845.10.1016/j.trb.2011.07.009
]Search in Google Scholar
[
Zhu, Y., Mao, B., Bai, Y. and Chen, S. (2017). A bi-level model for single-line rail timetable design with consideration of demand and capacity, Transportation Research C: Emerging Technologies 85: 211–233.10.1016/j.trc.2017.09.002
]Search in Google Scholar