IDEAS home Printed from
   My bibliography  Save this article

Factors affecting public transportation usage rate: Geographically weighted regression


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


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

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Coldren, Gregory M. & Koppelman, Frank S. & Kasturirangan, Krishnan & Mukherjee, Amit, 2003. "Modeling aggregate air-travel itinerary shares: logit model development at a major US airline," Journal of Air Transport Management, Elsevier, vol. 9(6), pages 361-369.
    2. Gutiérrez, Javier & Cardozo, Osvaldo Daniel & García-Palomares, Juan Carlos, 2011. "Transit ridership forecasting at station level: an approach based on distance-decay weighted regression," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1081-1092.
    3. Kuby, Michael & Barranda, Anthony & Upchurch, Christopher, 2004. "Factors influencing light-rail station boardings in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 223-247, March.
    4. Christopher R. Swimmer & Christopher C. Klein, 2010. "Public Transportation Ridership Levels," Journal for Economic Educators, Middle Tennessee State University, Business and Economic Research Center, vol. 10(1), pages 40-46, Summer.
    5. A S Fotheringham & M E Charlton & C Brunsdon, 1998. "Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis," Environment and Planning A, Pion Ltd, London, vol. 30(11), pages 1905-1927, November.
    6. Chris Lloyd & Ian Shuttleworth, 2005. "Analysing commuting using local regression techniques: scale, sensitivity, and geographical patterning," Environment and Planning A, Pion Ltd, London, vol. 37(1), pages 81-103, January.
    7. Buehler, Ralph, 2011. "Determinants of transport mode choice: a comparison of Germany and the USA," Journal of Transport Geography, Elsevier, vol. 19(4), pages 644-657.
    8. A S Fotheringham & M E Charlton & C Brunsdon, 1998. "Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis," Environment and Planning A, , vol. 30(11), pages 1905-1927, November.
    9. Souche, Stéphanie, 2010. "Measuring the structural determinants of urban travel demand," Transport Policy, Elsevier, vol. 17(3), pages 127-134, May.
    10. Chen, Cynthia & Chen, Jason & Barry, James, 2009. "Diurnal pattern of transit ridership: a case study of the New York City subway system," Journal of Transport Geography, Elsevier, vol. 17(3), pages 176-186.
    11. Blainey, Simon, 2010. "Trip end models of local rail demand in England and Wales," Journal of Transport Geography, Elsevier, vol. 18(1), pages 153-165.
    12. Boame, Attah K., 2004. "The technical efficiency of Canadian urban transit systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(5), pages 401-416, September.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Aston, Laura & Currie, Graham & Kamruzzaman, Md. & Delbosc, Alexa & Teller, David, 2020. "Study design impacts on built environment and transit use research," Journal of Transport Geography, Elsevier, vol. 82(C).
    2. Pritchard, John P. & Tomasiello, Diego Bogado & Giannotti, Mariana & Geurs, Karst, 2019. "Potential impacts of bike-and-ride on job accessibility and spatial equity in São Paulo, Brazil," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 386-400.
    3. Zhihua Zhang & Rachel J. C. Chen & Lee D. Han & Lu Yang, 2017. "Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach," Sustainability, MDPI, Open Access Journal, vol. 9(9), pages 1-13, September.
    4. Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-16, December.
    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).
    6. Guandong Su & Hidenori Okahashi & Lin Chen, 2018. "Spatial Pattern of Farmland Abandonment in Japan: Identification and Determinants," Sustainability, MDPI, Open Access Journal, vol. 10(10), pages 1-22, October.
    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.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:78:y:2015:i:c:p:161-177. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.