Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty

Published Online:https://doi.org/10.1287/trsc.2021.1040

References

  • Canca D, Barrena E, Laporte G, Ortega FA (2016) A short-turning policy for the management of demand disruptions in rapid transit systems. Ann. Oper. Res. 246(1-2):145–166.CrossrefGoogle Scholar
  • Cats O, Jenelius E (2015) Planning for the unexpected: The value of reserve capacity for public transport network robustness. Transportation Res. Part A: Policy Practice 81:47–61.CrossrefGoogle Scholar
  • Cheng YH, Tsao HL (2010) Rolling stock maintenance strategy selection, spares parts estimation, and replacements interval calculation. Internat. J. Production Econom. 128(1):404–412.CrossrefGoogle Scholar
  • Chintapalli P, Hazra J (2015) Pricing and inventory management during new product introduction when shortage creates hype. Naval Res. Logist. 62(4):304–320.CrossrefGoogle Scholar
  • Curbed New York (2017) How vulnerable are New York City’s underwater subway tunnels to flooding? Accessed Feburary 11, 2020, https://ny.curbed.com/2017/12/28/16807198/nyc-subway-tunnel-construction-water-damage.Google Scholar
  • De-Los-Santos A, Laporte G, Juan A Mesa FP (2012) Evaluating passenger robustness in a rail transit network. Transportation Res. Part C: Emerging Tech. 20(1):34–46.CrossrefGoogle Scholar
  • FailRail.sg (2012) The bigger story of a troubled rail network through data visualization. Accessed Feburary 11, 2020, https://failrailsg.appspot.com/.Google Scholar
  • Fischetti M, Salvagnin D, Zanette A (2009) Fast approaches to improve the robustness of a railway timetable. Transportation Sci. 43(3):321–335.LinkGoogle Scholar
  • Garib A, Radwan AE, Al-Deek H (1997) Estimating magnitude and duration of incident delays. J. Transportation Engrg. 123(6):459–466.CrossrefGoogle Scholar
  • Goh J, Sim M (2010) Distributionally robust optimization and its tractable approximations. Oper. Res. 58(4)902–917.LinkGoogle Scholar
  • Goody Feed (2019) 10 mrt stations that are so crowded during peak hours you would rather uber. Accessed Feburary 11, 2020, https://goodyfeed.com/10-mrt-stations-crowded-peak-hours-youd-rather-uber/.Google Scholar
  • Jin JG, Lu L, Sun L, Yin J (2015) Optimal allocation of protective resources in urban rail transit networks against intentional attacks. Transportation Res. Part E: Logist. Trans. Rev. 84(December):73–87.CrossrefGoogle Scholar
  • Jin JG, Tang LC, Sun L, Lee DH (2014) Enhancing metro network resilience via localized integration with bus services. Transportation Res. Part E: Logist. Trans. Rev. 63(March):17–30.CrossrefGoogle Scholar
  • Jones B, Janssen L, Mannering F (1991) Analysis of the frequency and duration of freeway accidents in Seattle. Accident Anal. Prevention. 23(4):239–255.CrossrefGoogle Scholar
  • Khadilkar H (2016) Data-enabled stochastic modeling for evaluating schedule robustness of railway networks. Transportation Sci. 51(4):1161–1176.LinkGoogle Scholar
  • Lau AH-L, Lau H-S (1988) Maximizing the probability of achieving a target profit in a two-product newsboy problem. Decision Sci. 19(2):392–408.CrossrefGoogle Scholar
  • Meng L, Zhou X (2011) Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach. Transportation Res. Part B: Methodological 45(7):1080–1102.CrossrefGoogle Scholar
  • Nam D, Mannering F (2000) An exploratory hazard-based analysis of highway incident duration. Transportation Res. Part A: Policy Practice 34(2):85–102.CrossrefGoogle Scholar
  • Nielsen LK, Kroon L, Maróti G (2012) A rolling horizon approach for disruption management of railway rolling stock. Euro. J. Oper. Res. 220(2):496–509.CrossrefGoogle Scholar
  • Rijden de Treinen (2010) Are the trains running? No! Accessed Feburary 11, 2020, https://www.rijdendetreinen.nl/en.Google Scholar
  • Scarf H (1959) Bayes solutions of the statistical inventory problem. Ann. Math. Statist. 30(2):490–508.CrossrefGoogle Scholar
  • Schmöcker JD, Cooper S, Adeney W (2005) Metro service delay recovery: comparison of strategies and constraints across systems. Transportation Res. Rec. 1930(1):30–37.CrossrefGoogle Scholar
  • Starita S, Scaparra MP (2016) Optimizing dynamic investment decisions for railway systems protection. Eur. J. Oper. Res. 248(2):543–557.CrossrefGoogle Scholar
  • Sun L, Lee D-H, Erath A, Huang X (2012) Using smart card data to extract passenger’s spatio-temporal density and train’s trajectory of mrt system. Proceedings ACM SIGKDD Internat. Workshop Urban Comput. ACM, 142–148.Google Scholar
  • Van der Hurk E, Kroon L, Maróti G (2018) Passenger advice and rolling stock rescheduling under uncertainty for disruption management. Transportation Sci. 52(6):1391–1411.LinkGoogle Scholar
  • Veelenturf, LP, Potthoff D, Huisman D, Kroon LG, Maróti G, Wagelmans APM (2014) A quasi-robust optimization approach for crew rescheduling. Transportation Sci. 50(1):204–215.LinkGoogle Scholar
  • World Economic Forum (2018) These are the 10 busiest metros in the world. Accessed Feburary 11, 2020, https://www.weforum.org/agenda/2018/11/these-are-the-world-s-10-busiest-metros/.Google Scholar
  • Xu L, Ng TS (2020) A robust mixed-integer linear programming model for mitigating rail transit disruptions under uncertainty. Transportation Sci. 54(5):1388–1407.LinkGoogle Scholar
  • Yang J, Jian GJ, Wu J, Jiang X (2017) Optimizing passenger flow control and bus-bridging service for commuting metro lines. Comput. Aided Civil Infrastructure Engrg. 32(6):458–473.CrossrefGoogle Scholar
  • Zhang S, Lo HK (2018) Metro disruption management: Optimal initiation time of substitute bus services under uncertain system recovery time. Transportation Res. Part C: Emerg. Tech. 97(December):409–427.CrossrefGoogle Scholar
  • Zilko, AA, Kurowicka D, Goverde RMP (2016) Modeling railway disruption lengths with copula bayesian networks. Transportation Res. Part C: Emerging Tech. 68(July):350–368.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.