4.5 Article

Using big data for last mile performance evaluation: An accessibility-based approach

期刊

TRAVEL BEHAVIOUR AND SOCIETY
卷 25, 期 -, 页码 153-163

出版社

ELSEVIER
DOI: 10.1016/j.tbs.2021.06.003

关键词

Last mile; Trip destination; Transit equity; Public transit; Big data

资金

  1. Shanghai Educational Development Foundation under Chenguang Program [19CG77]
  2. National Natural Science Foundation of China [52000130]

向作者/读者索取更多资源

This paper evaluates the last mile performance of public transportation using an accessibility-based approach, finding high variance in last mile performance in Chicago, with areas of low performance typically clustering in economically disadvantaged areas. Income levels and housing sale price are positively related to last mile performance.
Objective: The 'last mile' of public transportation describes the final leg of a transit journey. This paper brings an accessibility-based approach to last-mile performance evaluation at the parcel level by measuring desirable destinations reasonably reachable through accessible transit stations. Methods: An accessibility-based last mile performance measure is developed to include destinations, attractiveness, and transit connectivity. Google Map API data is used to identify potential destinations and further evaluate their popularity. Results: The range of last-mile performance scores was 0-91.7954%, with a mean of 49.82% and a standard deviation of 61.61%, indicating high variation of the last mile performance in Chicago. Last mile problem areas in Chicago tend to cluster in more economically challenged areas. Income levels and housing sale price had positive relationships with last mile performance scores. Conclusion: Areas with low last-mile accessibility performance are more likely to cluster in communities that have greater economic disadvantages, lower density, and less mixed land use, implying spatial inequality and disparity in overall accessibility. Practice: The described approach can inform the development of strategic planning interventions to improve transit connectivity and to reduce the disparity of transit connectivity and accessibility across neighborhoods. Implications: The evaluation of last mile connectivity needs to consider both access to transit station and access to potential destinations. The last mile performance score is highly influenced by neighborhood socioeconomic status.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据