Factors affecting public transportation usage rate: Geographically weighted regression

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

*Corresponding author for this work

研究成果: Article同行評審

35 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)161-177
頁數17
期刊Transportation Research Part A: Policy and Practice
78
DOIs
出版狀態Published - 1 八月 2015

指紋 深入研究「Factors affecting public transportation usage rate: Geographically weighted regression」主題。共同形成了獨特的指紋。

引用此