TY - JOUR
T1 - Understanding the interplay between bus, metro, and cab ridership dynamics in Shenzhen, China
AU - Yue, Mengxue
AU - Kang, Chaogui
AU - Andris, Clio
AU - Qin, Kun
AU - Liu, Yu
AU - Meng, Qingxiang
N1 - Funding Information:
National Key Research and Development Project of China, Grant/Award No.: 2017YFB0503604; National Natural Sci ence Foundation of China, Grant/Award No.: 41401484; China Postdoctoral Sci ence Foundation, Grant/Award No.: 2015M580666 and 2017T100569; The Hong Kong Polytechnic University, Grant/ Award No.: 4-ZZFZ; Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of China, Grant/Award No.: 15S01
Publisher Copyright:
© 2018 John Wiley & Sons Ltd
PY - 2018/6
Y1 - 2018/6
N2 - The most common mass transit modes in metropolitan cities include buses, subways, and taxicabs, each of which contribute to an interconnected complex network that delivers urban dwellers to their destinations. Understanding the intertwined usages of these three transit modes at different places and time allows for better sensing of urban mobility and the built environment. In this article, we leverage a comprehensive data collection of bus, metro, and taxicab ridership from Shenzhen, China to unveil the spatio-temporal interplay between different mass transit modes. To achieve this goal, we develop a novel spectral clustering framework that imposes spatio-temporal similarities between mass transit mode usage in urban space and differentiates urban spaces associated with distinct ridership patterns of mass transit modes. Five resulting categories of urban spaces are identified and interpreted with auxiliary knowledge of the city's metro network and land-use functionality. In general, different categorized urban spaces are associated with different accessibility levels (such as high-, medium-, and low-ranked) and different urban functionalities (such as residential, commercial, leisure-dominant, and home–work balanced). The results indicate that different mass transit modes cooperate or compete based on demographic and socioeconomic attributes of the underlying urban environments. Our proposed analytical framework provides a novel and effective way to explore the mass transit system and the functional heterogeneity in cities. It demonstrates great potential for assisting policymakers and municipal managers in optimizing public transportation facility allocation and city-wide daily commuting distribution.
AB - The most common mass transit modes in metropolitan cities include buses, subways, and taxicabs, each of which contribute to an interconnected complex network that delivers urban dwellers to their destinations. Understanding the intertwined usages of these three transit modes at different places and time allows for better sensing of urban mobility and the built environment. In this article, we leverage a comprehensive data collection of bus, metro, and taxicab ridership from Shenzhen, China to unveil the spatio-temporal interplay between different mass transit modes. To achieve this goal, we develop a novel spectral clustering framework that imposes spatio-temporal similarities between mass transit mode usage in urban space and differentiates urban spaces associated with distinct ridership patterns of mass transit modes. Five resulting categories of urban spaces are identified and interpreted with auxiliary knowledge of the city's metro network and land-use functionality. In general, different categorized urban spaces are associated with different accessibility levels (such as high-, medium-, and low-ranked) and different urban functionalities (such as residential, commercial, leisure-dominant, and home–work balanced). The results indicate that different mass transit modes cooperate or compete based on demographic and socioeconomic attributes of the underlying urban environments. Our proposed analytical framework provides a novel and effective way to explore the mass transit system and the functional heterogeneity in cities. It demonstrates great potential for assisting policymakers and municipal managers in optimizing public transportation facility allocation and city-wide daily commuting distribution.
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U2 - 10.1111/tgis.12340
DO - 10.1111/tgis.12340
M3 - Article
AN - SCOPUS:85051333651
SN - 1361-1682
VL - 22
SP - 855
EP - 871
JO - Transactions in GIS
JF - Transactions in GIS
IS - 3
ER -