Passenger Advice and Rolling Stock Rescheduling Under Uncertainty for Disruption Management
Published Online:9 Oct 2018https://doi.org/10.1287/trsc.2017.0759
References
- (2014) Multimodal route choice models of public transport passengers in the greater Copenhagen area. Eur. J. Transportation Logist. 6(3):1–25.Google Scholar
- (2003) Applications of operations research in the air transport industry. Transportation Sci. 37(4):368–391.Link, Google Scholar
- (2006) Flight operations recovery: New approaches considering passenger recovery. J. Scheduling 9(3):279–298.Crossref, Google Scholar
- (2012) Railway rolling stock planning: Robustness against large disruptions. Transportation Sci. 46(2):217–232.Link, Google Scholar
- (2014) An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Res. Part B: Methodological 63:15–37.Crossref, Google Scholar
- (2011) Robust rolling stock in rapid transit networks. Comput. Oper. Res. 38(8):1131–1142.Crossref, Google Scholar
- (2013) Recovery of disruptions in rapid transit networks. Transportation Res. Part E: Logist. Transportation Rev. 53:15–33.Crossref, Google Scholar
- (2015) Smooth and controlled recovery planning of disruptions in rapid transit networks. IEEE Trans. Intelligent Transportation Systems 16(4):2192–2202.Crossref, Google Scholar
- (1986) Bus network design. Transportation Res. Part B: Methodological 20(2):331–344.Crossref, Google Scholar
- (2017) Integrating train scheduling and delay management in real-time railway traffic control. Transportation Res. Part E: Logist. Transportation Rev. 105:213–239.Crossref, Google Scholar
- (2015) A Lagrangian heuristic framework for a real-life integrated planning problem of railway transportation resources. Transportation Res. Part B: Methodological 74:138–150.Crossref, Google Scholar
- (2012) Delay management with rerouting of passengers. Transportation Sci. 46(2):74–89.Link, Google Scholar
- (2006) A rolling stock circulation model for combining and splitting of passenger trains. Eur. J. Oper. Res. 174(2):1281–1297.Crossref, Google Scholar
- (2015) Reactive robustness and integrated approaches for railway optimization problems. Unpublished doctoral thesis, DTU Management Engineering, Technical University of Denmark, Lyngby, Denmark.Google Scholar
- (2016) A comparison of two exact methods for passenger railway rolling stock (re)scheduling. Transportation Res. Part E: Logist. Transportation Rev. 91:15–32.Crossref, Google Scholar
- (2016) Integrated recovery of aircraft and passengers after airline operation disruption based on a {GRASP} algorithm. Transportation Res. Part E: Logist. Transportation Rev. 87:97–112.Crossref, Google Scholar
- (2015) Optimization of multi-fleet aircraft routing considering passenger transiting under airline disruption. Comput. Indust. Engrg. 80:132–144.Crossref, Google Scholar
- (2009) Disruption management in passenger railway transportation. Ahuja RK, Möhring RH, Zaroliagis C, eds. Robust and Online Large-Scale Optimization—Models and Techniques for Transportation Systems, Lecture Notes Comput. Sci., Vol. 5868 (Springer, Berlin Heidelberg), 399–421.Crossref, Google Scholar
- (2007) Airline disruption management: Perspectives, experiences and outlook. J. Air Transport Management 13(3):149–162.Crossref, Google Scholar
- (2011) Effect of real-time transit information on dynamic path choice of passengers. Transportation Res. Record 2217:46–54.Crossref, Google Scholar
- (2014) Rescheduling of railway rolling stock with dynamic passenger flows. Transportation Sci. 49(2):165–184.Link, Google Scholar
- (2006) Planning for robust airline operations: Optimizing aircraft routings and flight departure times to minimize passenger disruptions. Transportation Sci. 40(2):15–28.Link, Google Scholar
- (2016) Solving the integrated airline recovery problem using column-and-row generation. Transportation Sci. 50(1):216–239.Link, Google Scholar
- (2012) A rolling horizon approach for disruption management of railway rolling stock. Eur. J. Oper. Res. 220(2):496–509.Crossref, Google Scholar
- (2000) A stochastic transit assignment model considering differences in passengers utility functions. Transportation Res. Part B: Methodological 34(5):377–402.Crossref, Google Scholar
- (2014) User perspectives in public transport timetable optimisation. Transportation Res. Part C: Emerging Tech. 48:269–284.Crossref, Google Scholar
- (2015) Passenger perspectives in railway timetabling: A literature review. Transportation Rev. 36(4):500–526.Crossref, Google Scholar
- (2009) Route choice modeling: Past, present and future research directions. J. Choice Modelling 2(1):65–100.Crossref, Google Scholar
- (2015) A review of real time railway traffic management during disturbances. Corman F, Voß S, Negenborn RR, eds. Computational Logistics, Lecture Notes Comput. Sci., Vol. 9335 (Springer International Publishing, Cham, Switzerland), 658–672.Crossref, Google Scholar
- (2016) A variable neighbourhood search for fast train scheduling and routing during disturbed railway traffic situations. Comput. Oper. Res. 78:480–499.Crossref, Google Scholar
- (2013) A mip-based timetable rescheduling formulation and algorithm minimizing further inconvenience to passengers. J. Rail Transport Planning Management 3(3):38–53.Crossref, Google Scholar
- (2007) Integer programming approaches for solving the delay management problem. Geraets F, Kroon LG, Schöbel A, Wagner D, Zaroliagis CD, eds. Algorithmic Methods for Railway Optimization, Lecture Notes Comput. Sci., Vol. 4359 (Springer, Berlin Heidelberg), 145–170.Google Scholar
- (2016) Shuttle planning for link closures in urban public transport networks. Transportation Sci. 50(3):947–965.Link, Google Scholar
- (2017) Passenger oriented railway disruption management by adapting timetables and rolling stock schedules. Transportation Res. Part C: Emerging Tech. 80(July):133–147.Crossref, Google Scholar
- (2016) A quasi-robust optimization approach for crew rescheduling. Transportation Sci. 50(1):204–215.Link, Google Scholar
- (2011) Where is my bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders. Transportation Res. Part A: Policy Practice 45(8):839–848.Crossref, Google Scholar

