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Machine learning approach for study on subway passenger flow
3 references·2 citing·22 second hop
Machine learning approach for study on subway passenger flow (2022)Machine learning approach for study on su…IS2Fun: Identification of Subway Station Functions Using Massive Urban Data (2017)IS2Fun: Identification of Sub…Smart card data use in public transit: A literature review (2011)Smart card data use in public…Diurnal pattern of transit ridership: a case study of the New York City subway system (2008)Diurnal pattern of transit ri…Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient (2024)Predicting Station-Level Peak…Differential Dynamics of Transit Use Resilience During the COVID-19 Pandemic Using Multivariate Two-Dimensional Functional Data Analysis (2024)Differential Dynamics of Tran…Property, Privacy, and Personal Data (2004)Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile (2012)Mining smart card data for transit riders’ travel patterns (2013)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Transportation–land-use interaction: empirical findings in North America, and their implications for modeling (2000)Factors influencing light-rail station boardings in the United States (2004)Exploring time variants for short-term passenger flow (2010)How does commuting behavior change due to incentives? An empirical study of the Beijing Subway System (2014)Identifying the Structure of Cities by Clustering Using a New Similarity Measure Based on Smart Card Data (2019)Discovering Urban Functional Zones Using Latent Activity Trajectories (2014)Passenger Segmentation Using Smart Card Data (2014)Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing (2017)Spatial Analysis of Bikeshare Ridership With Smart Card and POI Data Using Geographically Weighted Regression Method (2018)Considering context and dynamics: A classification of transit-orientated development for New York City (2020)Concordance between Regional Functions and Mobility Features Using Bike-sharing and Land-use Data near Metro Stations (2022)The Scree Test For The Number Of Factors (1966)On the notion of regional economic resilience: conceptualization and explanation (2014)How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands (2020)Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea (2010)Application of geographically weighted regression to the direct forecasting of transit ridership at station-level (2012)Effect of green technology innovation on green total factor productivity in China: Evidence from spatial durbin model analysis (2020)
this work references cited by second hop

References

  • 2011Smart card data use in public transit: A literature review930 cited
  • 2008Diurnal pattern of transit ridership: a case study of the New York City subway system96 cited
  • 2017IS2Fun: Identification of Subway Station Functions Using Massive Urban Data52 cited

Cited by

  • 2024Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient5 cited
  • 2024Differential Dynamics of Transit Use Resilience During the COVID-19 Pandemic Using Multivariate Two-Dimensional Functional Data Analysis3 cited

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