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An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environment

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References

2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited
2018A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership333 cited
2015Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul320 cited
2019How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds311 cited
2020Examining the relationship between built environment and metro ridership at station-to-station level280 cited
2006Alternative Approaches to Modeling the Travel-Demand Impacts of Smart Growth263 cited
2013What influences Metro station ridership in China? Insights from Nanjing260 cited
2021Public transit use in the United States in the era of COVID-19: Transit riders’ travel behavior in the COVID-19 impact and recovery period220 cited
2019Understanding the impact of built environment on metro ridership using open source in Shanghai214 cited
2011An analysis of Metro ridership at the station-to-station level in Seoul197 cited
2022Time-varying and non-linear associations between metro ridership and the built environment185 cited
2021Changes in local travel behaviour before and during the COVID-19 pandemic in Hong Kong182 cited
2020The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou

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