2020
Gan, Z.; Yang, M.; Feng, T.; Timmermans, H.
Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations Journal Article
In: Transportation, vol. 47, iss. 1, 2020, ISSN: 15729435.
Abstract | Links | BibTeX | Tags: LCLU, Ridership patterns, Smart card data, Station clustering, Urban mobility
@article{Gan2020b,
title = {Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations},
author = {Z. Gan and M. Yang and T. Feng and H. Timmermans},
doi = {10.1007/s11116-018-9885-4},
issn = {15729435},
year = {2020},
date = {2020-01-01},
journal = {Transportation},
volume = {47},
issue = {1},
abstract = {Smart card data derived from automatic fare collection (AFC) systems of public transit enable us to study resident movement from a macro perspective. The rhythms of traffic generated by different land uses differ, reflecting differences in human activity patterns. Thus, an understanding of daily ridership and mobility patterns requires an understanding of the relationship between daily ridership patterns and characteristics of stations and their direct environment. Unfortunately, few studies have investigated this relationship. This study aims to propose a framework of identifying urban mobility patterns and urban dynamics from a spatiotemporal perspective and pointing out the linkages between mobility and land cover/land use (LCLU). Relying on 1 month’s transactions data from the AFC system of Nanjing metro, the 110 metro stations are classified into 7 clusters named as employment-oriented stations, residential-oriented stations, spatial mismatched stations, etc., each characterized by a distinct ridership pattern (combining boarding and alighting). A comparison of the peak hourly ridership of the seven clusters is conducted to verify whether the clustering results are reasonable or not. Finally, a multinomial logit model is used to estimate the relationship between characteristics of the local environment and cluster membership. Results show that the classification based on ridership patterns leads to meaningful interpretable clusters and that significant associations exist between local LCLU characteristics, distance to the city center and cluster membership. The analytical framework and findings may be beneficial for improving service efficiency of public transportation and urban planning.},
keywords = {LCLU, Ridership patterns, Smart card data, Station clustering, Urban mobility},
pubstate = {published},
tppubtype = {article}
}
Smart card data derived from automatic fare collection (AFC) systems of public transit enable us to study resident movement from a macro perspective. The rhythms of traffic generated by different land uses differ, reflecting differences in human activity patterns. Thus, an understanding of daily ridership and mobility patterns requires an understanding of the relationship between daily ridership patterns and characteristics of stations and their direct environment. Unfortunately, few studies have investigated this relationship. This study aims to propose a framework of identifying urban mobility patterns and urban dynamics from a spatiotemporal perspective and pointing out the linkages between mobility and land cover/land use (LCLU). Relying on 1 month’s transactions data from the AFC system of Nanjing metro, the 110 metro stations are classified into 7 clusters named as employment-oriented stations, residential-oriented stations, spatial mismatched stations, etc., each characterized by a distinct ridership pattern (combining boarding and alighting). A comparison of the peak hourly ridership of the seven clusters is conducted to verify whether the clustering results are reasonable or not. Finally, a multinomial logit model is used to estimate the relationship between characteristics of the local environment and cluster membership. Results show that the classification based on ridership patterns leads to meaningful interpretable clusters and that significant associations exist between local LCLU characteristics, distance to the city center and cluster membership. The analytical framework and findings may be beneficial for improving service efficiency of public transportation and urban planning.