A Hybrid Control Strategy for a Dynamic Scheduling Problem in Transit Networks

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. Open 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. Open 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. Open 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