ABSTRACT
Asian cities have promoted TOD to increase MRT use and decrease private vehicle use. A crucial factor in achieving this goal is improving the pedestrian environment, which can increase people's willingness to walk to MRT stations. This paper analyses how Asian cities have improved the pedestrian environment around MRT stations and the factors influencing walking willingness by reviewing literature and taking Taipei as the empirical area. The results demonstrated that reducing obstacles in pedestrian space, evening the pavement, using transparent window displays in stores along the pavement, and constructing rain shelters can increase people's willingness to walk. The simulations of improved environments can provide references for countries improving the pedestrian environment. The contribution and innovation are to point out the differences in the motivation and goals of TOD in Asian and Western cities and propose improvements to increase people's willingness to walk to achieve TOD's goals.
Acknowledgement
We would like to thank the editors and anonymous reviewers for their valuable and constructive suggestions for improving this paper.
Author contributions
Conceptualisation: K.-C. H. and S.-W. L.; investigation, resources and data curation: Y.-Y. L.; methodology, software, and validation: S.-W. L. and Y.-Y. L.; writing—original draft preparation: K.-C. H. and S.-W. L.; writing—review and editing: K.-C. H., S.-W. L. and J.-H. Y.; simulation picture corrections: J.-H. Y. All authors have read and agreed to the published version of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 The significance level was 0.05. The t values of g(μi) and g(μi2) of Ycome were 7.39 and 6.19, respectively, and were both significant. The t values of g(μi) and g(μi2) of Yback were 8.30 and 6.75, respectively, and were both significant. Therefore, the variance and average of the WTP were not equal, and the t value was positive. Consequently, the test results indicate that the negative binomial regression model must be applied.