Integrated Timetabling and Scheduling of Modular Autonomous Vehicles Under Uncertainty

Published Online:https://doi.org/10.1287/trsc.2025.0116

References

  • Amberg B, Amberg B (2023) Robust and cost-efficient integrated multiple depot vehicle and crew scheduling with controlled trip shifting. Transportation Sci. 57(1):82–105.LinkGoogle Scholar
  • An K (2020) Battery electric bus infrastructure planning under demand uncertainty. Transportation Res. Part C Emerging Tech. 111:572–587.CrossrefGoogle Scholar
  • Angulo G, Ahmed S, Dey SS (2016) Improving the integer L-shaped method. INFORMS J. Comput. 28(3):483–499.LinkGoogle Scholar
  • Caprara A, Fischetti M, Toth P (2002) Modeling and solving the train timetabling problem. Oper. Res. 50(5):851–861.LinkGoogle Scholar
  • Carli R, Cavone G, Pippia T, De Schutter B, Dotoli M (2022) Robust optimal control for demand side management of multi-carrier microgrids. IEEE Trans. Automation Sci. Engrg. 19(3):1338–1351.CrossrefGoogle Scholar
  • Chen Z, Li X (2021) Designing corridor systems with modular autonomous vehicles enabling station-wise docking: Discrete modeling method. Transportation Res. Part E Logist. Transportation Rev. 152:102388.CrossrefGoogle Scholar
  • Chen Z, Li X, Qu X (2022) A continuous model for designing corridor systems with modular autonomous vehicles enabling station-wise docking. Transportation Sci. 56(1):1–30.LinkGoogle Scholar
  • Chen Z, Li X, Zhou X (2019) Operational design for shuttle systems with modular vehicles under oversaturated traffic: Discrete modeling method. Transportation Res. Part B Methodological 122:1–19.CrossrefGoogle Scholar
  • Chen Z, Li X, Zhou X (2020) Operational design for shuttle systems with modular vehicles under oversaturated traffic: Continuous modeling method. Transportation Res. Part B Methodological 132:76–100.CrossrefGoogle Scholar
  • Cho J, Papavasiliou A (2023) Pricing under uncertainty in multi-interval real-time markets. Oper. Res. 71(6):1928–1942.LinkGoogle Scholar
  • Huisman D, Freling R, Wagelmans APM (2004) A robust solution approach to the dynamic vehicle scheduling problem. Transportation Sci. 38(4):447–458.LinkGoogle Scholar
  • Ibarra-Rojas OJ, Giesen R, Rios-Solis YA (2014) An integrated approach for timetabling and vehicle scheduling problems to analyze the trade-off between level of service and operating costs of transit networks. Transportation Res. Part B Methodological 70:35–46.CrossrefGoogle Scholar
  • Laporte G, Louveaux FV (1993) The integer L-shaped method for stochastic integer programs with complete recourse. Oper. Res. Lett. 13(3):133–142.CrossrefGoogle Scholar
  • Laporte G, Ortega FA, Pozo MA, Puerto J (2017) Multi-objective integration of timetables, vehicle schedules and user routings in a transit network. Transportation Res. Part B Methodological 98:94–112.CrossrefGoogle Scholar
  • Lee K, Jiang Y, Ceder AA, Dauwels J, Su R, Nielsen OA (2022) Path-oriented synchronized transit scheduling using time-dependent data. Transportation Res. Part C Emerging Tech. 136:103505.CrossrefGoogle Scholar
  • Lyu G, Cheung WC, Teo CP, Wang H (2024) Multiobjective stochastic optimization: A case of real-time matching in ride-sourcing markets. Manufacturing Service Oper. Management 26(2):500–518.LinkGoogle Scholar
  • Ma Q, Li S, Zhang H, Yuan Y, Yang L (2021) Robust optimal predictive control for real-time bus regulation strategy with passenger demand uncertainties in urban rapid transit. Transportation Res. Part C Emerging Tech. 127:103086.CrossrefGoogle Scholar
  • Michaelis M, Schöbel A (2009) Integrating line planning, timetabling, and vehicle scheduling: A customer-oriented heuristic. Public Transport 1(3):211–232. CrossrefGoogle Scholar
  • Next Future Inc. (2023a) Dubai experiments the future of transportation, with NEXT. Accessed July 9, 2023, https://www.next-future-mobility.com/post/dubai-with-next-experiments-with-the-future-of-transportation.Google Scholar
  • Next Future Inc. (2023b) NEXT announces a new investment and partnership in UAE. Accessed October 10, 2023, https://www.next-future-mobility.com/post/next-announces-a-new-investment-and-partnership-in-uae.Google Scholar
  • Next Future Inc. (2023c) One vehicle, many use cases. Accessed September 19, 2025, https://www.next-future-mobility.com/maas.Google Scholar
  • Sánchez-Martínez G, Koutsopoulos H, Wilson N (2016) Real-time holding control for high-frequency transit with dynamics. Transportation Res. Part B Methodological 83:1–19.CrossrefGoogle Scholar
  • Shi X, Li X (2021) Operations design of modular vehicles on an oversaturated corridor with first-in, first-out passenger queueing. Transportation Sci. 55(5):1187–1205.LinkGoogle Scholar
  • Tian Q, Lin YH, Wang DZ (2023) Joint scheduling and formation design for modular-vehicle transit service with time-dependent demand. Transportation Res. Part C Emerging Tech. 147:103986.CrossrefGoogle Scholar
  • Van Lieshout RN (2021) Integrated periodic timetabling and vehicle circulation scheduling. Transportation Sci. 55(3):768–790.LinkGoogle Scholar
  • Van Lieshout RN, Bouman PC, Van den Akker M, Huisman D (2021) A self-organizing policy for vehicle dispatching in public transit systems with multiple lines. Transportation Res. Part B Methodological 152:46–64.CrossrefGoogle Scholar
  • Wang Y, Zhu S, Li S, Yang L, De Schutter B (2022) Hierarchical model predictive control for on-line high-speed railway delay management and train control in a dynamic operations environment. IEEE Trans. Control Systems Tech. 30(6):2344–2359.CrossrefGoogle Scholar
  • Xia D, Ma J, Sharif Azadeh S (2024) Integrated timetabling and vehicle scheduling of an intermodal urban transit network: A distributionally robust optimization approach. Transportation Res. Part C Emerging Tech. 162:104610.CrossrefGoogle Scholar
  • Xia D, Ma J, Sharif Azadeh S, Zhang W (2023) Data-driven distributionally robust timetabling and dynamic-capacity allocation for automated bus systems with modular vehicles. Transportation Res. Part C Emerging Tech. 155:104314.CrossrefGoogle Scholar
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