A multi-activity view of intra-urban travel networks: A case study of Beijing

Liu, Jian; Meng, Bin; Shi, Changsheng

doi:10.1016/j.cities.2023.104634

Cited by 4 publications

(2 citation statements)

References 61 publications

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“…Considering the characteristics of the spatial interaction network formed by daily travel flows, as well as the variability and directionality of travel flows between spatial units, we use Degree Centrality to quantify the importance of different traffic network nodes [50]. The higher the degree of centrality, the more routes pass through the station, and the more trips the station has [51].…”

Section: Nodal Parameters Of the Network: Indicators Of Dynamic Traff...mentioning

confidence: 99%

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

Mei,

Wang,

Gong

et al. 2024

Sustainability

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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.

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Section: Nodal Parameters Of the Network: Indicators Of Dynamic Traff...mentioning

confidence: 99%

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

Mei,

Wang,

Gong

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Existing research on the urban network structure from the perspective of traffic flow, focusing on internal street units, is primarily based on spatial interaction networks, which rely on data from sources such as mobile phone signaling [3], GPS trajectories [4], subways [5], and bus card swipes [6]. However, the findings are subject to certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Liang,

Xie,

Bao

2024

Sustainability

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Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

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Understanding regional structure through spatial networks: A simulation optimization framework for exploring balanced development

Li,

Liao,

et al. 2024

Habitat International

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A multi-activity view of intra-urban travel networks: A case study of Beijing

Cited by 4 publications

References 61 publications

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

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

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Understanding regional structure through spatial networks: A simulation optimization framework for exploring balanced development

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