2022
Liu, Yang; Ji, Yanjie; Feng, Tao; Shi, Zhuangbin
A route analysis of metro-bikeshare users using smart card data Journal Article
In: Travel Behaviour and Society, vol. 26, pp. 108-120, 2022, ISSN: 2214-367X.
Abstract | Links | BibTeX | Tags: Metro-bikeshare integration, Smart card data, Spatiotemporal patterns, Travel route
@article{LIU2022108,
title = {A route analysis of metro-bikeshare users using smart card data},
author = {Yang Liu and Yanjie Ji and Tao Feng and Zhuangbin Shi},
url = {https://www.sciencedirect.com/science/article/pii/S2214367X21000880},
doi = {https://doi.org/10.1016/j.tbs.2021.09.006},
issn = {2214-367X},
year = {2022},
date = {2022-01-01},
journal = {Travel Behaviour and Society},
volume = {26},
pages = {108-120},
abstract = {Few studies have analyzed individuals’ travel route characteristics in the integrated metro and bikeshare network. Taking Nanjing, China, as a case study, this paper analyzes the combined travel route of metro-bikeshare users based on three-week metro- and shared bike smart card data. The smart card data provides both boarding/borrowing and alighting/returning location and time, which makes it possible to trace each combined mode user’s actual metro routes. By assuming that the bike routes are the shortest paths between a specific OD pair from smart card records, we extract the combined metro-bikeshare travel routes of each user by using a metro route reconstruction method. The results show that over 60% of the metro-bikeshare users, most of whom are youth adults (18–35 years old), only occasionally use this mode. Further, the spatial and temporal characteristics of the combined metro-bikeshare travel routes are analyzed visually across different age groups. We find that most of the combined metro-bikeshare routes concentrated on Metro Line 2 with different cycling access/egress routes; however, the routes mostly travelled by different age groups are significantly different. Young adults are mainly distributed in suburban and exurban areas. Adults are more likely to be metro-bikeshare commuters, with travel routes usually passing through the central area of the city. Besides, the routes mostly travelled by the elderly are longer than those by other groups, and highly coincide with the routes mostly travelled by adults during the rush hours. Finally, relevant policy implementations are proposed in conjunction with metro-bikeshare users’ travel route characteristics.},
keywords = {Metro-bikeshare integration, Smart card data, Spatiotemporal patterns, Travel route},
pubstate = {published},
tppubtype = {article}
}
Few studies have analyzed individuals’ travel route characteristics in the integrated metro and bikeshare network. Taking Nanjing, China, as a case study, this paper analyzes the combined travel route of metro-bikeshare users based on three-week metro- and shared bike smart card data. The smart card data provides both boarding/borrowing and alighting/returning location and time, which makes it possible to trace each combined mode user’s actual metro routes. By assuming that the bike routes are the shortest paths between a specific OD pair from smart card records, we extract the combined metro-bikeshare travel routes of each user by using a metro route reconstruction method. The results show that over 60% of the metro-bikeshare users, most of whom are youth adults (18–35 years old), only occasionally use this mode. Further, the spatial and temporal characteristics of the combined metro-bikeshare travel routes are analyzed visually across different age groups. We find that most of the combined metro-bikeshare routes concentrated on Metro Line 2 with different cycling access/egress routes; however, the routes mostly travelled by different age groups are significantly different. Young adults are mainly distributed in suburban and exurban areas. Adults are more likely to be metro-bikeshare commuters, with travel routes usually passing through the central area of the city. Besides, the routes mostly travelled by the elderly are longer than those by other groups, and highly coincide with the routes mostly travelled by adults during the rush hours. Finally, relevant policy implementations are proposed in conjunction with metro-bikeshare users’ travel route characteristics.