Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

Z. Gan, M. Yang (Corresponding author), T. Feng, H.J.P. Timmermans

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Smart card data derived from automatic fare collection (AFC) systems of pub-lic 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 under-standing 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 mobil-ity 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).
Original languageEnglish
Number of pages22
JournalTransportation
DOIs
Publication statusE-pub ahead of print - 2020

Fingerprint

Land use
pricing
land use
Smart cards
transaction
Macros
resident
activity pattern
land cover
human activity
station

Keywords

  • Urban mobility
  • Ridership patterns
  • Smart card data
  • Station clustering

Cite this

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title = "Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations",
abstract = "Smart card data derived from automatic fare collection (AFC) systems of pub-lic 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 under-standing 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 mobil-ity 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).",
keywords = "Urban mobility, Ridership patterns, Smart card data, Station clustering",
author = "Z. Gan and M. Yang and T. Feng and H.J.P. Timmermans",
year = "2020",
doi = "10.1007/s11116-018-9885-4",
language = "English",
journal = "Transportation",
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Understanding urban mobility patterns from a spatiotemporal perspective : daily ridership profiles of metro stations. / Gan, Z.; Yang, M. (Corresponding author); Feng, T.; Timmermans, H.J.P.

In: Transportation, 2020.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Understanding urban mobility patterns from a spatiotemporal perspective

T2 - daily ridership profiles of metro stations

AU - Gan, Z.

AU - Yang, M.

AU - Feng, T.

AU - Timmermans, H.J.P.

PY - 2020

Y1 - 2020

N2 - Smart card data derived from automatic fare collection (AFC) systems of pub-lic 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 under-standing 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 mobil-ity 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).

AB - Smart card data derived from automatic fare collection (AFC) systems of pub-lic 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 under-standing 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 mobil-ity 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).

KW - Urban mobility

KW - Ridership patterns

KW - Smart card data

KW - Station clustering

U2 - 10.1007/s11116-018-9885-4

DO - 10.1007/s11116-018-9885-4

M3 - Article

JO - Transportation

JF - Transportation

SN - 0049-4488

ER -