Unraveling the Impact of Spatial Configuration on TOD Function Mix Use and Spatial Intensity: An Analysis of 47 Morning Top-Flow Stations in Beijing (2018–2020)
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- Jinkyung Choi & Yong Lee & Taewan Kim & Keemin Sohn, 2012. "An analysis of Metro ridership at the station-to-station level in Seoul," Transportation, Springer, vol. 39(3), pages 705-722, May.
- Cervero, Robert, 1994. "Transit-based housing in California: evidence on ridership impacts," Transport Policy, Elsevier, vol. 1(3), pages 174-183, June.
- Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
- Kwoka, Gregory J. & Boschmann, E. Eric & Goetz, Andrew R., 2015. "The impact of transit station areas on the travel behaviors of workers in Denver, Colorado," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 277-287.
- Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
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Keywords
top-flow stations; built environment of station area; spatial configuration; spatial function; spatial intensity;All these keywords.
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