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Development of a Behavior-Based Passenger Flow Assignment Model for Urban Rail Transit in Section Interruption Circumstance
3 references·12 citing·47 second hop
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this work references cited by second hop

References

  • 1986Urban transportation networks: Equilibrium analysis with mathematical programming methods2,352 cited
  • 2012Model of Passenger Flow Assignment for Urban Rail Transit Based on Entry and Exit Time Constraints78 cited
  • 2012Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data37 cited

Cited by

  • 2021Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: A case study of four London Tube strikes26 cited
  • 2024A modeling framework to integrate frequency - and schedule-based passenger assignment approaches for coordinated path choice and space-time trajectory estimation based on multi-source observations20 cited
  • 2018Subway user behaviour when affected by incidents in Toronto (SUBWAIT) survey — A joint revealed preference and stated preference survey with a trip planner tool19 cited
  • 2022Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach18 cited
  • 2020A stated-preference intercept survey of transit-rider response to service disruptions17 cited
  • 2024Understanding Heterogeneous Passenger Route Choice in Municipal Rail Transit with Express and Local Trains: An Empirical Study in Shanghai11 cited
  • 2016Transit User Behaviour in Response to Service Disruption: State of Knowledge11 cited
  • 2015Application of Geographic Information System (GIS) in Urban Rail Transit Construction Safety and Operation Monitoring9 cited

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2021
A Data-Driven Urban Metro Management Approach for Crowd Density Control
8 cited
  • 2022Effects of tube service disruptions on the usage of the London Cycle Hire6 cited
  • 2020Detecting latent urban mobility structure using mobile phone data6 cited
  • 2023Travel behavior shifts under extreme system-level disruptions3 cited