SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
The effect of unexpected disruptions and information times on public transport passengers: a simulation study
3 references·4 citing·22 second hop
The effect of unexpected disruptions and information times on public transport passengers: a simulation study (2020)The effect of unexpected disruptions and …The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data (2019)The MATSim Open Berlin Scenar…Improving predictions of public transport usage during disturbances based on smart card data (2017)Improving predictions of publ…The Multi-Agent Transport Simulation MATSim (2016)The Multi-Agent Transport Sim…Resilience of public transport in the face of disruptions: Insights from explainable machine learning (2025)Resilience of public transpor…Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach (2023)Understanding passenger trave…Towards a more realistic simulation of public transit: Generating transit schedules with vehicle circulations (2021)Towards a more realistic simu…Modeling and Quantifying Interaction of Information and Capacity in Public Transport Disruptions (2021)Modeling and Quantifying Inte…The demand for public transport: a practical guide (2004)Smart card data use in public transit: A literature review (2011)Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile (2012)Station-based average travel distance and its relationship with urban form and land use: An analysis of smart card data in Nanjing City, China (2019)Impacts of long-term service disruptions on passenger travel behaviour: A smart card analysis from the Greater Copenhagen area (2021)Evaluating the flooding level impacts on urban metro networks and travel demand: behavioral analyses, agent-based simulation, and large-scale case study (2022)Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns (2004)A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City (2020)Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data (2021)Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models (2021)Adding Transit to an Agent-Based Transportation Simulation: Concepts and Implementation (2010)Robustness as a Third Dimension for Evaluating Public Transport Plans (2018)Analysis of transit users’ waiting tolerance in response to unplanned service disruptions (2019)Developing a passive GPS tracking system to study long-term travel behavior (2019)Predicting traffic volumes and estimating the effects of shocks in massive transportation systems (2015)Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework (2022)Passenger Route and Departure Time Guidance Under Disruptions in Oversaturated Urban Rail Transit Networks (2024)Predicting disruptions and their passenger delay impacts for public transport stops (2020)Robustness and disturbances in public transport (2022)Transport modelling in the age of big data (2017)Dynamic cities: Location-based accessibility modelling as a function of time (2018)A Review of Public Transport Economics (2020)
this work references cited by second hop

References

  • 2016The Multi-Agent Transport Simulation MATSim1,064 cited
  • 2019The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data169 cited
  • 2017Improving predictions of public transport usage during disturbances based on smart card data45 cited

Cited by

  • 2023Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach8 cited
  • 2025Resilience of public transport in the face of disruptions: Insights from explainable machine learning7 cited
  • 2021Modeling and Quantifying Interaction of Information and Capacity in Public Transport Disruptions6 cited
  • 2021Towards a more realistic simulation of public transit: Generating transit schedules with vehicle circulations4 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work