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A spatial statistical approach to estimate bus stop demand using GIS-processed data
16 references·1 citing·48 second hop
A spatial statistical approach to estimate bus stop demand using GIS-processed data (2024)A spatial statistical approach to estimat…All ridership is local: Accessibility, competition, and stop-level determinants of daily bus boardings in Portland, Oregon (2022)All ridership is local: Acces…Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership (2020)Who left riding transit? 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References

  • 1970A Computer Movie Simulating Urban Growth in the Detroit Region9,754 cited
  • 1993Statistics for Spatial Data700 cited
  • 2004Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects555 cited
  • 2013New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas390 cited
  • 2020Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership271 cited
  • 2006Alternative Approaches to Modeling the Travel-Demand Impacts of Smart Growth263 cited
  • 2016Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal145 cited
  • 2009Pedestrian Environments and Transit Ridership119 cited
  • 2013Geographically Weighted Negative Binomial Regression—incorporating overdispersion117 cited
  • 2010Direct Ridership Model of Bus Rapid Transit in Los Angeles County, California107 cited
  • 2019Urban bus ridership, income, and extreme weather events72 cited
  • 2003Bus Transit and Land Use: Illuminating the Interaction70 cited
  • 2012Assessment of Models to Estimate Bus-Stop Level Transit Ridership using Spatial Modeling Methods61 cited
  • 2004RIDERSHIP MODELS AT THE STOP LEVEL55 cited

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2022
All ridership is local: Accessibility, competition, and stop-level determinants of daily bus boardings in Portland, Oregon
41 cited
  • 2015Factors Influencing Stop-Level Transit Ridership in Arnhem–Nijmegen City Region, Netherlands36 cited
  • Cited by

    • 2025A novel multimodal deep learning-based direct ridership model for planning-oriented demand prediction in urban rail transit networks6 cited