Citation

Ma X, Ji Y, Yang M, Jin Y, Tan X. Transp. Policy 2018; 71: 57-69.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.tranpol.2018.07.008

PMID

unavailable

Abstract

Though metro systems are established in many Chinese cities including Nanjing, they have yet covered every corner of a city. Bikeshare as a feeder mode to metro helps solve the last mile problem. Thus, it is necessary to monitor and analyze metro-bikeshare transfer characteristics. The primary objective of this study is to derive a reproducible methodology that isolates bicycle-metro transfer trips using smart card data. Two recognition rules proposed are a maximum transfer time of 10 min and a maximum transfer distance of 300 m. To explore the general characteristics of metro-bikeshare transfer trips, transfer stations served at less than 30 transfer trips during three consecutive weeks were eliminated to ensure that a non-typical transfer pattern would not distort the results. The results show that more than 89% passengers recognized have less than 6 transfers in 3 weeks, indicating that most users integrate bikeshare with metro impromptu. Two transfer peaks on workdays are during 7:00-9:00 and 17:00-19:00, especially in suburban areas, while at weekends, transfers show quite even during 8:00-19:00. As to "Return-Enter" and "Exit-Lease" transfer modes, the "time difference" phenomenon does exist, which means that the transfer peak of "Return-Enter"mode always happens one hour earlier than that of "Exit-Lease". Furthermore, the demographic differences in metro-bikeshare usage pattern are revealed. Finally, policy implications are involved to improve the performance of metro-bikeshare integration for all kinds of people without creating inequality.

Keywords

Bikeshare; Metro-bikeshare transfer; Policy implications; Smart card data; Transfer pattern; Visualization