[1]

Venter CJ. 2020. Measuring the quality of the first/last mile connection to public transport. Research in Transportation Economics 83:100949

doi: 10.1016/j.retrec.2020.100949
[2]

Liu W, Pang S, Li W, Han Y. 2025. Assessing the CO2 emission reduction potential of metro-bus combined travel through interpretable machine learning. Transportmetrica A: Transport Science 1−24

doi: 10.1080/23249935.2025.2472869
[3]

Ewing R, Cervero R. 2001. Travel and the built environment: a synthesis. Transportation Research Record: Journal of the Transportation Research Board 1780:87−114

doi: 10.3141/1780-10
[4]

Seaborn C, Attanucci J, Wilson NHM. 2009. Analyzing multimodal public transport journeys in London with smart card fare payment data. Transportation Research Record: Journal of the Transportation Research Board 2121(1):55−62

doi: 10.3141/2121-06
[5]

Pelletier MP, Trepanier M, Morency C. 2011. Smart card data use in public transit: a literature review. Transportation Research Part C: Emerging Technologies 19(4):557−568

doi: 10.1016/j.trc.2010.12.003
[6]

Ma X, Wu Y, Wang Y, Chen F, Liu J. 2013. Mining smart card data for transit riders' travel patterns. Transportation Research Part C: Emerging Technologies 36:1−12

doi: 10.1016/j.trc.2013.07.010
[7]

He Y, Zhao Y, Tsui KL. 2018. An analysis of factors influencing metro station ridership: insights from Taipei Metro. arXiv Preprint:1904.01280

doi: 10.48550/arXiv.1904.01280
[8]

Gilbert B, Ogburn EL, Datta A. 2023. Consistency of common spatial estimators under spatial confounding. arXiv Preprint:230812181

doi: 10.48550/arXiv.2308.12181
[9]

Fotheringham AS, Brunsdon C, Charlton M. 1998. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30(11):1905−1927

doi: 10.1068/a301905
[10]

Wardman M. 2001. A review of British evidence on time and service quality valuations. Transportation Research Part E: Logistics and Transportation Review 37(2–3):107−128

doi: 10.1016/s1366-5545(00)00012-0
[11]

Zhao D, Wang W, Woodburn A, Ryerson MS. 2017. Isolating high-priority metro and feeder bus transfers using smart card data. Transportation 44(6):1535−1554

doi: 10.1007/s11116-016-9713-7
[12]

Ewing R, Cervero R. 2010. Travel and the built environment: a meta-analysis. Journal of the American Planning Association 76(3):265−294

doi: 10.1080/01944361003766766
[13]

Cervero R, Kockelman K. 1997. Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment 2(3):199−219

doi: 10.1016/S1361-9209(97)00009-6
[14]

Li S, Lyu D, Huang G, Zhang X, Gao F, Chen Y, et al. 2020. Spatially varying impacts of built environment factors on rail transit ridership at station level: a case study in Guangzhou, China. Journal of Transport Geography 82:102631

doi: 10.1016/j.jtrangeo.2019.102631
[15]

Diao M. 2019. Towards sustainable urban transport in Singapore: policy instruments and mobility trends. Transport Policy 81:320−330

doi: 10.1016/j.tranpol.2018.05.005
[16]

Fotheringham AS, Brunsdon C, Charlton M. 2002. Geographically weighted regression: the analysis of spatially varying relationships. US: John Wiley & Sons. 269 pp.

[17]

Cardozo OD, García-Palomares JC, Gutiérrez J. 2012. Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography 34:548−558

doi: 10.1016/j.apgeog.2012.01.005
[18]

Wu J, Xu J, Xu D. 2025. Short-term inbound passenger flow forecasting for urban rail transit based on phase space reconstruction and deep learning. Digital Transportation and Safety 4(3):177−187

doi: 10.48130/dts-0025-0015
[19]

Yuan K, Cui D, Long J. 2023. Bus frequency optimization in a large-scale multi-modal transportation system: integrating 3D-MFD and dynamic traffic assignment. Digital Transportation and Safety 2(4):241−252

doi: 10.48130/DTS-2023-0020
[20]

Sui X, Yan H, Pan S, Li X, Gu X. 2024. Bus system optimization for timetables, routes, charging, and facilities: a summary. Digital Transportation and Safety 4(1):1−9

doi: 10.48130/dts-0024-0024
[21]

Zhou X, Gao Y. 2017. "Shanghai 2035" Green transport development plans and strategies. UTC, China. Available from: www.chinautc.com/upload/fckeditor/%E5%91%A8%E7%BF%94.pdf

[22]

Li W, Chen S, Dong J, Wu J. 2021. Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros. Journal of Transport Geography 92:103032

doi: 10.1016/j.jtrangeo.2021.103032
[23]

National Bureau of Statistics. 2012. Tabulation on the 2010 Population Census of the People's Republic of China. Beijing: China Statistics Press. www.stats.gov.cn

[24]

OpenStreetMap contributors. 2015. OpenStreetMap data. www.openstreetmap.org (Accessed: 2025-02-16)

[25]

Gaode Maps. 2015. Gaode maps open platform. https://lbs.amap.com/. (Accessed: 2025-02-16)

[26]

Shi Y, Zeng L. 2025. How do built environment characteristics influence metro-bus transfer patterns across metro station types in Shanghai? Journal of Transport Geography 123:104137

doi: 10.1016/j.jtrangeo.2025.104137
[27]

Bagchi M, White PR. 2005. The potential of public transport smart card data. Transport Policy 12(5):464−474

doi: 10.1016/j.tranpol.2005.06.008
[28]

Draper NR, Smith H. 1998. Applied regression analysis, 3rd edition. New York: Wiley. doi: 10.1002/9781118625590

[29]

Han D, Choi CG. 2025. The spatial dynamics of urban vegetation and housing prices: insights from pre- and post-pandemic Chicago using OLS and MGWR models. PLoS One 20(9):e0330932

doi: 10.1371/journal.pone.0330932
[30]

Klar R, Rubensson I. 2024. Spatio-temporal investigation of public transport demand using smart card data. Applied Spatial Analysis and Policy 17(1):241−268

doi: 10.1007/s12061-023-09542-x
[31]

He Y, Zhao Y, Tsui KL. 2021. An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership. Transportation 48(3):1185−1216

doi: 10.1007/s11116-020-10091-2
[32]

Yang H, Xu T, Chen D, Yang H, Pu L. 2020. Direct modeling of subway ridership at the station level: a study based on mixed geographically weighted regression. Canadian Journal of Civil Engineering 47(5):534−545

doi: 10.1139/cjce-2018-0727