Abstract
Abstract The last mile between metro stations and commuters’ homes or workplaces has become one of key topics in relation to metro ridership in megacities where more than 10 million people live. Al...
Highlights
The last mile between metro stations and the homes or workplaces of commuters has become a central topic in relation to metro ridership in megacities
We asked three questions: (1) Which factors influence the mode choice between cycling and walking? (2) What types of routes do commuters prefer when cycling to a metro station? (3) For what distance are public bicycles primarily used as feeder mode, and do they apply to solving the last-mile problem? Based on the binary logit modeling results and statistical analysis, three points are discussed to understand the travel behavior and preference of commuters when connecting their origins/destinations with metro stations
Promoting the integration of public bicycle and the metro system provides an effective solution for the last-mile problem
Summary
The last mile between metro stations and the homes or workplaces of commuters has become a central topic in relation to metro ridership in megacities. Regarding the different origins and destinations of different commuters and various travel preferences, it is difficult but necessary for governments and city planners to develop applicable solutions for the last-mile problem. Transport services for the last-mile problem would directly affect the accessibility of the metro system (Zuo et al 2020). Many commuters in megacities may choose to live far away from metro stations in order to reduce housing costs, which leads to a long-distance commute (Zhao et al 2020). Under such circumstances, providing a better last-mile service for long-distance commuters is conducive to social welfare. At present, experts and scholars have mainly studied the last-mile issue from the logistics distribution perspective (Halldórsson and Wehner 2020; Janjevic and Winkenbach 2020; Kapser and Abdelrahman 2020), only a few studies have dealt with this issue from the perspective of individual travel behavior (Li et al 2019; Zuo et al 2020)
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