Quantification and control of disruption propagation in multi-level public transport networks

Altazin, 2020, A multi-objective optimization-simulation approach for real time rescheduling in dense railways, Eur. J. Oper. Res., 286, 662, 10.1016/j.ejor.2020.03.034 Binder, 2017, The multi-objective railway timetable rescheduling problem, Transp. Res. Part C, 78, 78, 10.1016/j.trc.2017.02.001 Cacchiani, V., Huisman, D., Kidd, M., Kroon, L., Toth, P., Veelenturf, L., Wagenaar, J., An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B 63: 15–37. Canca, 2016, Setting lines frequency and capacity in dense railway rapid transit networks with simultaneous passenger assignment, Transp. Res. Part B, 93, 251, 10.1016/j.trb.2016.07.020 Cats, 2010, Mesoscopic modelling of bus transportation, Transp. Res. Rec., 2188, 9, 10.3141/2188-02 Cats, 2014, Dynamic vulnerability analysis of public transport networks: mitigation effects of real-time information, Networks and Spatial Economics, 14, 435, 10.1007/s11067-014-9237-7 Cats, 2016, Planning for the unexpected: the value of reserve capacity for public transport robustness, Transp. Res. Part A, 81, 47 Cats, 2018, Beyond a complete failure: the impact of partial capacity degradation on public transport network vulnerability, Transportmetrica B: Transport Dynamics, 6, 77 Cats, 2016, A dynamic stochastic model for evaluating congestion and crowding effects in transit systems, Transp. Res. Part B, 89, 43, 10.1016/j.trb.2016.04.001 Corman, 2020, Interactions and equilibrium between rescheduling train traffic and routing passengers in microscopic delay management: A game theoretical study, Transp. Sci., 54, 10.1287/trsc.2020.0979 Corman, 2010, A tabu search algorithm for rerouting trains during rail operations, Transp. Res. Part B, 44, 175, 10.1016/j.trb.2009.05.004 Corman, 2017, Integrating train rescheduling and delay management in real-time railway traffic control, Transp. Res. Part E, 105, 213, 10.1016/j.tre.2016.04.007 D’Ariano, 2007, A branch and bound algorithm for scheduling trains in a railway network, Eur. J. Oper. Res., 183, 643, 10.1016/j.ejor.2006.10.034 Daganzo, C.F., Anderson, P., 2016. Coordinating Transit Transfers in Real Time. Institute of Transportation Studies, UC Berkeley. https://escholarship.org/uc/item/25h4r974. Delgado, 2012, How much can holding and/or limiting boarding improve transit performance?, Transp. Res. Part B, 46, 1202, 10.1016/j.trb.2012.04.005 Delgado, 2009, Real-time control of buses in a transit corridor based on vehicle holding and boarding limits, Transp. Res. Rec., 2090, 59, 10.3141/2090-07 Desaulniers, G., Hickman, M.D., 2007. Public Transit. In C. Barnhart and G. Laporte (Eds.), Handbook in OR & MS (pp. 69-127). Amsterdam, the Netherlands: Elsevier. Dollevoet, 2014, An iterative optimization framework for delay management and train rescheduling, Flexible Services and Manufacturing Journal, 26, 490, 10.1007/s10696-013-9187-2 Dollevoet, 2011, Delay management with rerouting of passengers, Transp. Sci., 46 Dowling R., Skabardonis, A., Alexiadis, V., 2004. Traffic analysis toolbox volume III: Guidelines for applying traffic microsimulation modeling software. U.S. Department of Transportation, Federal Highway Administration (FHA), Washington DC. Available at: http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol3/index.htm. Gavriilidou, 2019, Reconciling transfer synchronization and service regularity: real-time control strategies using passenger data, Transportmetrica A, 15, 215, 10.1080/23249935.2018.1458757 Ghaemi, 2018, Impact of railway disruption predictions and rescheduling on passenger delays, J. Rail Transp. Plann. Manage., 8, 103 Gordon, 2013, Automated inference of linked transit journeys in London using fare-transaction and vehicle location data, Transp. Res. Record: J. Transp. Res. Board, 2343, 17, 10.3141/2343-03 Ghosh, S., Lee, T., 2000. Intelligent transportation systems: New principles and architectures. Stability of RYNSORD under perturbations (pp. 109-132). Boca Raton, FL: CRC Press LCC. Hadas, 2010, Optimal coordination of public transit vehicles using operational tactics examined by simulation, Transp. Res. Part C, 18, 879, 10.1016/j.trc.2010.04.002 Hörcher, 2017, Crowding cost estimation with large scale smart card and vehicle location data, Transp. Res. Part B, 95, 105, 10.1016/j.trb.2016.10.015 Jenelius, 2015, The value of new public transport links for network robustness and redundancy, Transportmetrica A: Transport Science, 11, 819, 10.1080/23249935.2015.1087232 Kang, 2015, A case study on the coordination of last trains for the Beijing subway network, Transp. Res. Part B, 72, 112, 10.1016/j.trb.2014.09.003 Laskaris, 2018, Multiline holding based control for lines merging to a shared corridor, Transportmetrica B: Transport Dynamics, 7, 1062 Leng, N., De Martinis, V., Corman, F., 2018. Agent-based simulation approach for disruption management in rail schedule. 14th Conference on Advanced Systems in Public Transport (CASPT): Brisbane, Australia Liu, 2020, Collaborative optimization for metro train rescheduling and train connections combined with passenger flow control strategy, Omega, 90, 10.1016/j.omega.2018.10.020 Long, 2020, Synchronizing last trains of urban rail transit system to better serve passengers from late night train of high-speed railway lines, Networks and Spatial Economics, 20, 599, 10.1007/s11067-019-09487-0 Malandri, 2018, Recovery time and propagation effects of passenger transport disruption, Phys. A, 505, 7, 10.1016/j.physa.2018.03.028 Munizaga, 2012, Estimation of a disaggregate multimodal public transport origin-destination matrix from passive smart card data from Santiago, Chile, Transp. Res. Part C, 24, 9, 10.1016/j.trc.2012.01.007 Obrenovic, N., 2019. TRANS-FORM D3.3: Tool for evaluating real-time strategies. TRANS-FORM project report. Lausanne: Switzerland. Oliveira, 2016, Indicators of reliability and vulnerability: Similarities and differences in ranking links of a complex road system, Transp. Res. Part A, 88, 195 Paulsen, M., Rasmussen, T.K., Anker Nielsen, O., 2018. Modelling railway-induced passenger delays in multi-modal public transport networks. 14th Conference on Advanced Systems in Public Transport (CASPT): Brisbane, Australia. Sanchez-Martinez, 2016, Real-time holding control for high-frequency transit with dynamics, Transp. Res. Part B, 83, 1, 10.1016/j.trb.2015.11.013 Schmidt, 2015, The complexity of integrating passenger routing decisions in public transportation models, Networks, 65, 228, 10.1002/net.21600 Schön, 2018, A stochastic dynamic programming approach for delay management of a single train line, Eur. J. Oper. Res., 271, 501, 10.1016/j.ejor.2018.05.031 Schmaranzer, 2019, Population-based simulation optimization for urban mass rapid transit networks, Flexible Services and Manufacturing Journal Shakibayifar, 2017, A simulation-based optimization approach to reschedule train traffic in uncertain conditions during disruptions, Scientia Iranica: Int. J. Sci. Technol., 25, 646 TAG Unit M3.2. 2020. Public transport assignment. Department for Transport: UK. Tirachini, 2017, Estimation of crowding discomfort in public transport: Results from Santiago de Chile, Transp. Res. Part A, 103, 311 Törnquist Krasemann, 2012, Design of an effective algorithm for fast response to the re-scheduling of railway traffic during disturbances, Transp. Res. Part C, 20, 62, 10.1016/j.trc.2010.12.004 Törnquist, 2007, N-tracked railway traffic re-scheduling during disturbances, Transp. Res. Part B, 41, 342, 10.1016/j.trb.2006.06.001 Van der Hurk, 2018, Passenger advice and rolling stock rescheduling under uncertainty for disruption management, Transp. Sci., 52, 1391, 10.1287/trsc.2017.0759 Van Oort, 2017, Ridership evaluation and prediction in public transport by processing smart card data: a Dutch approach and example Yap, 2017, A robust transfer inference algorithm for public transport journeys during disruptions, Transp. Res. Procedia, 27, 1042, 10.1016/j.trpro.2017.12.099 Yap, M.D., Cats, O., van Arem, B., 2018a. Crowding valuation in urban tram and bus transportation based on smart card data. Transportmetrica A: Transport Science, doi: 10.1080/23249935. 2018.1537319. Yap, 2018, Improving predictions of public transport usage during disturbances based on smart card data, Transp. Policy, 61, 84, 10.1016/j.tranpol.2017.10.010 Yap, 2018, Identification and quantification of link vulnerability in multi-level public transport networks: a passenger perspective, Transportation, 45, 1161, 10.1007/s11116-018-9892-5 Yap, 2019, Where shall we sync? Clustering passenger flows to identify urban public transport hubs and their key synchronization priorities, Transp. Res. Part C, 98, 433, 10.1016/j.trc.2018.12.013 Yin, 2016, Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach, Transp. Res. Part B, 91, 178, 10.1016/j.trb.2016.05.009 Younan, B., Wilson, N.H.M., 2010. Improving Transit Service Connectivity: The Application of Operations Planning and Control Strategies. 12th WCTRS (World Conference on Transport Research Society), Lisbon, Portugal. Zhu, 2019, Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions, Transp. Res. Part B, 123, 149, 10.1016/j.trb.2019.02.015