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Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios
2 references·16 citing·52 second hop
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References

  • 2010The Geography of Taste: Analyzing Cell-Phone Mobility and Social Events280 cited
  • 2011Measuring social functions of city regions from large-scale taxi behaviors153 cited

Cited by

  • 2018Big data and understanding change in the context of planning transport systems103 cited
  • 2019Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions88 cited
  • 2021Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method79 cited
  • 2019Combining ITS and optimization in public transportation planning: state of the art and future research paths59 cited
  • 2021Review of Transit Data Sources: Potentials, Challenges and Complementarity56 cited
  • 2024An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques55 cited
  • 2016A Bayesian Additive Model for Understanding Public Transport Usage in Special Events53 cited
  • 2015Use of Geotagged Social Media in Urban Settings: Empirical Evidence on Its Potential from Twitter47 cited
  • 2020Personalized predictive public transport crowding information with automated data sources45 cited
  • 2016Mapping Social Media for Transportation Studies43 cited
  • 2016Singapore in Motion39 cited
  • 2015A utility-maximization model for retrieving users’ willingness to travel for participating in activities from big-data

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31 cited
  • 2019Network–wide prediction of public transportation ridership using spatio–temporal link–level information20 cited
  • 2022Effectiveness of trip planner data in predicting short-term bus ridership16 cited
  • 2018The impact of Planned Special Events (PSEs) on urban traffic congestion15 cited
  • 2021Generating demand responsive bus routes from social network data analysis14 cited