Integrated Optimization of Transit Networks with Schedule- and Frequency-Based Services Subject to the Bounded Stochastic User Equilibrium

Published Online:https://doi.org/10.1287/trsc.2022.1148

References

  • Adjiman CS, Androulakis IP, Floudas CA (1998) A global optimisation method, αBB, for general twice-differentiable constrained NLPs-II. Implementation and computational results. Comput. Chemical Engrg. 22(9):1159–1179.CrossrefGoogle Scholar
  • Anderson MK, Nielsen OA, Prato CG (2017) Multimodal route choice models of public transport passengers in the Greater Copenhagen Area. EURO J. Transportation Logist. 6(3):221–245.CrossrefGoogle Scholar
  • Ávila-Torres P, López-Irarragorri F, Caballero R, Ríos-Solís Y (2018) The multimodal and multiperiod urban transportation integrated timetable construction problem with demand uncertainty. J. Indust. Management Optim. 14(2):447–472.CrossrefGoogle Scholar
  • Bertsimas D, Ng YS, Yan J (2020) Joint frequency-setting and pricing optimization on multimodal transit networks at scale. Transportation Sci. 54(3):839–853.LinkGoogle Scholar
  • Bertsimas D, Ng YS, Yan J (2021) Data-driven transit network design at scale. Oper. Res. 69(4):1118–1133.LinkGoogle Scholar
  • Bonami P, Kilinç M, Linderoth J (2012) Algorithms and software for convex mixed integer nonlinear programs. Lee J, Leyffer S, eds. Mixed Integer Nonlinear Programming (Springer, New York), 1–39.CrossrefGoogle Scholar
  • Borndörfer R, Grötschel M, Pfetsch ME (2007) A column-generation approach to line planning in public transport. Transportation Sci. 41(1):123–132.LinkGoogle Scholar
  • Braess D, Nagurney A, Wakolbinger T (2005) On a paradox of traffic planning. Transportation Sci. 39(4):446–450.LinkGoogle Scholar
  • Cancela H, Mauttone A, Urquhart ME (2015) Mathematical programming formulations for transit network design. Transportation Res. Part B Methodological 77(July):17–37.CrossrefGoogle Scholar
  • Cats O, West J, Eliasson J (2016) A dynamic stochastic model for evaluating congestion and crowding effects in transit systems. Transportation Res. Part B Methodological 89(July):43–57.CrossrefGoogle Scholar
  • Ceder A, Jiang Y (2020) Route guidance ranking procedures with human perception consideration for personalized public transport service. Transportation Res. Part C Emerging Tech 118(September):102667.Google Scholar
  • Ceder A, Tal O (2001) Designing synchronisation into bus timetables. Transporation Res. Record 1760(1):28–33.CrossrefGoogle Scholar
  • Ceder A, Wilson NH (1986) Bus network design. Transportation Res. Part B Methodological 20(4):331–344.CrossrefGoogle Scholar
  • Ceder A, Golany B, Tal O (2001) Creating bus timetables with maximal synchronisation. Transportation Res. Part A Policy Practice 35(10):913–928.CrossrefGoogle 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(April):1–19.CrossrefGoogle Scholar
  • Constantin I, Florian M (1995) Optimising frequencies in a transit network: A nonlinear bi‐level programming approach. Internat. Trans. Oper. Res. 2(2):149–164.CrossrefGoogle Scholar
  • Corman F (2020) Interactions and equilibrium between rescheduling train traffic and routing passengers in microscopic delay management: A game theoretical study. Transportation Sci. 54(3):785–822.LinkGoogle Scholar
  • Cortés CE, Jara-Moroni P, Moreno E, Pineda C (2013) Stochastic transit equilibrium. Transportation Res. Part B Methodological 51(May):29–44.CrossrefGoogle Scholar
  • De Cea J, Fernández E (1993) Transit assignment for congested public transport systems: An equilibrium model. Transportation Sci. 27(2):133–147.LinkGoogle Scholar
  • Dell’Olio L, Ibeas A, Cecin P (2011) The quality of service desired by public transport users. Transport Policy 18(1):217–227.CrossrefGoogle Scholar
  • Eltved M, Nielsen OA, Rasmussen TK (2019) An assignment model for public transport networks with both schedule-and frequency-based services. EURO J. Transportation Logist. 8(5):769–793.CrossrefGoogle Scholar
  • Fan W, Machemehl RB (2006) Optimal transit route network design problem with variable transit demand: Genetic algorithm approach. J. Transportation Engrg. 132(1):40–51.CrossrefGoogle Scholar
  • Farahani RZ, Miandoabchi E, Szeto WY, Rashidi H (2013) A review of urban transportation network design problems. Eur. J. Oper. Res. 229(2):281–302.CrossrefGoogle Scholar
  • Florian M (2004) Finding shortest time-dependent paths in schedule-based transit networks: A label setting algorithm. Wilson NHM, Nuzzolo A, eds. Schedule-Based Dynamic Transit Modeling: Theory and Applications (Springer, Boston), 43–52.CrossrefGoogle Scholar
  • Fouilhoux P, Ibarra-Rojas OJ, Kedad-Sidhoum S, Rios-Solis YA (2016) Valid inequalities for the synchronisation bus timetabling problem. Eur. J. Oper. Res. 251(2):442–450.CrossrefGoogle Scholar
  • Gardner CB, Nielsen SD, Eltved M, Rasmussen TK, Nielsen OA, Nielsen BF (2021) Calculating conditional passenger travel time distributions in mixed schedule-and frequency-based public transport networks using Markov chains. Transportation Res. Part B Methodological 152(October):1–17.CrossrefGoogle Scholar
  • Goerigk M, Schmidt M (2017) Line planning with user-optimal route choice. Eur. J. Oper. Res. 259(2):424–436.CrossrefGoogle Scholar
  • Guihaire V, Hao JK (2008) Transit network design and scheduling: A global review. Transportation Res. Part A Policy Practice 42(10):1251–1273.CrossrefGoogle Scholar
  • Guo X, Sun H, Wu J, Jin J, Zhou J, Gao Z (2017) Multiperiod-based timetable optimisation for metro transit networks. Transportation Res. Part B Methodological 96(February):46–67.CrossrefGoogle Scholar
  • Hamdouch Y, Ho HW, Sumalee A, Wang G (2011) Schedule-based transit assignment model with vehicle capacity and seat availability. Transportation Res. Part B Methodological 45(10):1805–1830.CrossrefGoogle Scholar
  • Hussain E, Bhaskar A, Chung E (2021) Transit OD matrix estimation using smartcard data: Recent developments and future research challenges. Transportation Res. Part C Emerging Tech. 125(April):103044.CrossrefGoogle Scholar
  • Ibarra-Rojas OJ, Rios-Solis YA (2012) Synchronisation of bus timetabling. Transportation Res. Part B Methodological 46(5):599–614.CrossrefGoogle Scholar
  • Ibarra-Rojas OJ, López-Irarragorri F, Rios-Solis YA (2016) Multiperiod bus timetabling. Transportation Sci. 50(3):805–822.LinkGoogle Scholar
  • Ibarra-Rojas OJ, Delgado F, Giesen R, Muñoz JC (2015) Planning, operation, and control of bus transport systems: A literature review. Transportation Res. Part B Methodological 77(July):38–75.CrossrefGoogle Scholar
  • Jiang Y, Ceder A (2021) Incorporating personalization and bounded rationality into stochastic transit assignment model. Transportation Res. Part C Emerging Tech.127(June):103127.Google Scholar
  • Lee K, Jiang Y, Ceder A, Dauwels J, Su R, Nielsen OA (2022) Path-oriented synchronized transit scheduling using time-dependent data. Transportation Res. Part C Emerging Tech. 136(March):103505.CrossrefGoogle Scholar
  • Leurent F, Chandakas E, Poulhès A (2014) A traffic assignment model for passenger transit on a capacitated network: Bi-layer framework, line sub-models and large-scale application. Transportation Res. Part C Emerging Tech. 47(October):3–27.CrossrefGoogle Scholar
  • Li ZC, Lam WH, Wong SC, Sumalee A (2010) An activity-based approach for scheduling multimodal transit services. Transportation 37(5):751–774.CrossrefGoogle Scholar
  • Liu H, Wang DZ (2015) Global optimisation method for network design problem with stochastic user equilibrium. Transportation Res. Part B Methodological 72(February):20–39.CrossrefGoogle Scholar
  • Marcotte P, Nguyen S, Schoeb A (2004) A strategic flow model of traffic assignment in static capacitated networks. Oper. Res. 52(2):191–212.LinkGoogle Scholar
  • Martínez H, Mauttone A, Urquhart ME (2014) Frequency optimisation in public transportation systems: Formulation and metaheuristic approach. Eur. J. Oper. Res. 236(1):27–36.CrossrefGoogle Scholar
  • Mesa JA, Ortega FA, Pozo MA (2014) Locating optimal timetables and vehicle schedules in a transit line. Ann. Oper. Res. 222(1):439–455.CrossrefGoogle Scholar
  • Nagurney A, Boyce D (2005) Preface to “on a paradox of traffic planning”. Transportation Sci. 39(4):443–445.LinkGoogle Scholar
  • Nielsen OA, Eltved M, Anderson MK, Prato CG (2021) Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers. Transportation Res. Part A Policy Practice 147(May):76–92.CrossrefGoogle Scholar
  • Nuzzolo A, Russo F, Crisalli U (2001) A doubly dynamic schedule-based assignment model for transit networks. Transportation Sci. 35(3):268–285.LinkGoogle Scholar
  • Schmidt M, Schöbel A (2015) Timetabling with passenger routing. OR Spectrum 37(1):75–97.CrossrefGoogle Scholar
  • Schmöcker JD, Bell MG, Kurauchi F (2008) A quasi-dynamic capacity constrained frequency-based transit assignment model. Transportation Res. Part B Methodological 42(10):925–945.CrossrefGoogle Scholar
  • Schmöcker JD, Fonzone A, Shimamoto H, Kurauchi F, Bell MG (2011) Frequency-based transit assignment considering seat capacities. Transportation Res. Part B Methodological 45(2):392–408.CrossrefGoogle Scholar
  • Schöbel A (2017) An eigenmodel for iterative line planning, timetabling and vehicle scheduling in public transportation. Transportation Res. Part C Emerging Tech. 74(January):348–365.CrossrefGoogle Scholar
  • Shafiei S, Saberi M, Vu HL (2020) Integration of departure time choice modeling and dynamic origin–destination demand estimation in a large-scale network. Transportation Res. Record 2674(9):972–981.CrossrefGoogle Scholar
  • Sheffi Y (1985) Urban Transportation Networks (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
  • Szeto WY, Jiang Y (2014) Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach. Transportation Res. Part B Methodological 67(September):235–263.CrossrefGoogle Scholar
  • Tang Y, Jiang Y, Yang H, Nielsen OA (2020) Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems. Transportation Res. Part B Methodological 138(August):247–267.CrossrefGoogle Scholar
  • Tirachini A, Hensher DA, Jara-Díaz SR (2010) Comparing operator and users costs of light rail, heavy rail and bus rapid transit over a radial public transport network. Res. Transportation Econom. 29(1):231–242.CrossrefGoogle Scholar
  • Tirachini A, Hurtubia R, Dekker T, Daziano RA (2017) Estimation of crowding discomfort in public transport: Results from Santiago de Chile. Transportation Res. Part A Policy Practice 103(September):311–326.CrossrefGoogle Scholar
  • Tirachini A, Sun L, Erath A, Chakirov A (2016) Valuation of sitting and standing in metro trains using revealed preferences. Transport Policy 47(April):94–104.CrossrefGoogle Scholar
  • Toledo T, Cats O, Burghout W, Koutsopoulos HN (2010) Mesoscopic simulation for transit operations. Transportation Res. Part C Emerging Tech. 18(6):896–908.CrossrefGoogle Scholar
  • Wardman M, Whelan G (2011) Twenty years of rail crowding valuation studies: Evidence and lessons from British experience. Transport Rev. 31(3):379–398.CrossrefGoogle Scholar
  • Watling DP, Rasmussen TK, Prato CG, Nielsen OA (2018) Stochastic user equilibrium with a bounded choice model. Transportation Res. Part B Methodological 114(August):254–280.CrossrefGoogle Scholar
  • Wong RCW, Yuen TWY, Fung KW, Leung JMY (2008) Optimising timetable synchronisation for rail mass transit. Transportation Sci. 42(1):57–69.LinkGoogle Scholar
  • Yin J, D’Ariano A, Wang Y, Yang L, Tang T (2021) Timetable coordination in a rail transit network with time-dependent passenger demand. Eur. J. Oper. Res. 295(1):183–202.CrossrefGoogle Scholar
  • Zhang C, Gao Y, Yang L, Gao Z, Qi J (2020) Joint optimisation of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation. Transportation Res. Part B Methodological 134(April):64–92.CrossrefGoogle Scholar
  • Zimmermann M, Frejinger E, Marcotte P (2021) A strategic Markovian traffic equilibrium model for capacitated networks. Transportation Sci. 55(3):574–591.LinkGoogle Scholar
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