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Factors affecting public transportation usage rate: Geographically weighted regression

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  • Chiou, Yu-Chiun
  • Jou, Rong-Chang
  • Yang, Cheng-Han

Abstract

As the number of private vehicles grows worldwide, so does air pollution and traffic congestion, which typically constrain economic development. To achieve transportation sustainability and continued economic development, the dependency on private vehicles must be decreased by increasing public transportation usage. However, without knowing the key factors that affect public transportation usage, developing strategies that effectively improve public transportation usage is impossible. Therefore, this study respectively applies global and local regression models to identify the key factors of usage rates for 348 regions (township or districts) in Taiwan. The global regression model, the Tobit regression model (TRM), is used to estimate one set of parameters that are associated with explanatory variables and explain regional differences in usage rates, while the local regression model, geographically weighted regression (GWR), estimates parameters differently depending on spatial correlations among neighbouring regions. By referencing related studies, 32 potential explanatory variables in four categories, social-economic, land use, public transportation, and private transportation, are chosen. Model performance is compared in terms of mean absolute percentage error (MAPE) and spatial autocorrelation coefficient (Moran’ I). Estimation results show that the GWR model has better prediction accuracy and better accommodation of spatial autocorrelation. Seven variables are significantly tested, and most have parameters that differ across regions in Taiwan. Based on these findings, strategies are proposed that improve public transportation usage.

Suggested Citation

  • Chiou, Yu-Chiun & Jou, Rong-Chang & Yang, Cheng-Han, 2015. "Factors affecting public transportation usage rate: Geographically weighted regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 161-177.
  • Handle: RePEc:eee:transa:v:78:y:2015:i:c:p:161-177
    DOI: 10.1016/j.tra.2015.05.016
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    References listed on IDEAS

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    5. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 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, Elsevier, vol. 82(C).
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    7. Yanjie Ji & Xinwei Ma & Mingyuan Yang & Yuchuan Jin & Liangpeng Gao, 2018. "Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach," Sustainability, MDPI, Open Access Journal, vol. 10(5), pages 1-23, May.
    8. Dubé, Jean & Andrianary, Eugénie & Assad-Déry, François & Poupart, Janie & Simard, Justine, 2018. "Exploring difference in value uplift resulting from new bus rapid transit routes within a medium size metropolitan area," Journal of Transport Geography, Elsevier, vol. 72(C), pages 258-269.
    9. Zhao, Pengjun & Cao, Yushu, 2020. "Commuting inequity and its determinants in Shanghai: New findings from big-data analytics," Transport Policy, Elsevier, vol. 92(C), pages 20-37.
    10. Ingvardson, Jesper Bláfoss & Nielsen, Otto Anker, 2018. "How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas," Journal of Transport Geography, Elsevier, vol. 72(C), pages 50-63.
    11. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    12. Deng, Haopeng & Li, Ye & Li, Wenxiang & Yu, Yuewu, 2016. "Urban transport social needs in China: Quantification with central government transit grant," Transport Policy, Elsevier, vol. 51(C), pages 126-139.

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