Urban Traffic Dominance: A Dynamic Assessment Using Multi-Source Data in Shanghai

Yuyang Mei (), Shenmin Wang (), Mengjie Gong and Jiazheng Chen
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Yuyang Mei: Changwang School of Honors, Nanjing University of Information Science & Technology, Nanjing 210044, China
Shenmin Wang: School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Mengjie Gong: School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
Jiazheng Chen: Changwang School of Honors, Nanjing University of Information Science & Technology, Nanjing 210044, China

Sustainability, 2024, vol. 16, issue 12, 1-23

Abstract: This study redefines the evaluation of urban traffic dominance by integrating complex network theory with multi-source spatiotemporal trajectory data, addressing the dynamic nature of various transportation modes, including public transit and shared mobility. Traditional traffic studies, which focus predominantly on static road traffic characteristics, overlook the fluid dynamics integral to urban transport systems. We introduce Relative Weighted Centrality (RWC) as a novel metric for quantifying dynamic traffic dominance, combining it with traditional static metrics to forge a comprehensive traffic dominance evaluation system. The results show the following: (1) Both static and dynamic traffic dominance display core-periphery structures centered around Huangpu District. (2) Dynamically, distinct variations in RWC emerge across different times and transport modes; during the early hours (0:00–6:00), shared bicycles show unique spatial distributions, the subway network experiences a notable decrease in RWC yet maintains its spatial pattern, and taxis exhibit intermediate characteristics. Conversely, the RWC for all modes generally increases during morning (6:00–12:00) and evening (18:00–24:00) peaks, with a pronounced decrease in subway RWC in the latter period. (3) The integration of dynamic evaluations significantly modifies conventional static results, emphasizing the impact of population movements on traffic dominance. This comprehensive analysis provides crucial insights into the strategic management and development of urban traffic infrastructure in Shanghai.

Keywords: traffic dominance; dynamic assessment; multi-source data; complex network theory (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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