Weighted complex network analysis of the Beijing subway system: Train and passenger flows

Feng, Jia; Li, Xiamiao; Mao, Baohua; Xu, Qi; Bai, Yun

doi:10.1016/j.physa.2017.01.085

Cited by 55 publications

(21 citation statements)

References 48 publications

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“…Similar heterogeneity of traffic flows was also found in other transportation systems, such as the Singapore railway system [13] and the bus transportation network in China [10]. Generally, train flows are appropriate indicators of passenger flows between node pairs [8], and thus, we may infer that passenger flows between city pairs also exhibit significant heterogeneity in CRN.…”

Section: The Scale-free Structure Of Crnsupporting

confidence: 68%

“…Thus, it is crucial to examine the structural characteristics of transportation infrastructures. Complex network theory has been widely used to analyze the structural properties of various real-world transportation networks, including airport networks [4][5][6], shipping networks [7], subway networks [8], bus networks [9,10], and railway networks [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Characterizing the Structure of the Railway Network in China: A Complex Weighted Network Approach

Cao

Feng

Jia

et al. 2019

Journal of Advanced Transportation

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Understanding the structure of the Chinese railway network (CRN) is crucial for maintaining its efficiency and planning its future development. To advance our knowledge of CRN, we modeled CRN as a complex weighted network and explored the structural characteristics of the network via statistical evaluations and spatial analysis. Our results show CRN as a small-world network whose train flow obeys power-law decaying, demonstrating that CRN is a mature transportation infrastructure with a scale-free structure. CRN also shows significant spatial heterogeneity and hierarchy in its regionally uneven train flow distribution. We then examined the nodal centralities of CRN using four topological measures: degree, strength, betweenness, and closeness. Nodal degree is positively correlated with strength, betweenness, and closeness. Unlike the common feature of a scale-free network, the most connected nodes in CRN are not necessarily the most central due to underlying geographical, political, and socioeconomic factors. We proposed an integrated measure based on the four centrality measures to identify the global role of each node and the multilayer structure of CRN and confirm that stable connections hold between different layers of CRN.

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Section: The Scale-free Structure Of Crnsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Characterizing the Structure of the Railway Network in China: A Complex Weighted Network Approach

Cao

Feng

Jia

et al. 2019

Journal of Advanced Transportation

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“…This has led to efforts to incorporate PT specific features into complex network analysis of PTNs, such as travel demand and service attributes (e.g., passenger flows, transfers, service frequency, travel times, etc.). More weighted complex network analyses have emerged to account for demand and supply patterns in PTNs (e.g., Soh et al, 2010;Haznagy et al, 2015;Feng et al, 2017). Furthermore, investigations into the vulnerability, robustness and (node and link) criticality of PTNs have explicitly considered passenger demand and flow assignment (e.g., Cats and Jenelius, 2014;Cats, 2016;Cats et al, 2016Cats et al, , 2017.…”

Section: Network Science Analysis Of Ptnsmentioning

confidence: 99%

Integrating network science and public transport accessibility analysis for comparative assessment

Luo

Cats

Lint

et al. 2019

Journal of Transport Geography

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Network science offers powerful concepts and methods for studying complex systems, such as public transport networks. However, many existing studies on complex network analysis of public transport networks were primarily motivated to test network science concepts using real-life networks. Consequently, important properties related to public transport services in these studies have been overlooked. This has led to claims made by transport researchers that these works do not necessarily improve the understanding of system properties and performance. To bridge this gap, this study integrates network science and public transport accessibility analysis for comparative assessment across multiple public transport networks. The primary contribution of this study pertains to developing a method based on network science for computing public transport accessibility measured as the average travel impedance. The travel impedance metric is defined based on the generalized travel cost between stop pairs, containing initial and transfer waiting times, in-vehicle travel times and time-equivalent transfer penalty costs. To perform efficient computation, we propose a new type of weighted graph representation of public transport networks, which explicitly incorporates travel costs that are derived from general transit feed specification (GTFS) data. Besides the methodological contribution, the secondary one of this study consists in performing a comparative assessment of worldwide tram networks' accessibility. The analysis shows insights into how different travel components (e.g., in-vehicle travel times and waiting and transfer times) specifically account for the variance in accessibility across different networks. Additional references for improving the designing and planning of public transport networks can be obtained from such comparative assessments.

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“…Using the measures, they found that Tokyo and Rome are the most robust networks because of the many options for transfers which provides shorter transfers and more route choices. Feng et al [10] combined traffic flows with the network measures. They used a multilayer model to analyze traffic flow patterns in the Beijing subway system and concluded that the model can help to better analyze the combined traffic flow and network operation.…”

Section: Network Performance Measuresmentioning

confidence: 99%

Network Analysis of the Tel Aviv Mass Transit Plan

2018

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This paper analyzes the strategic transit network plan for the Tel Aviv metropolitan area, using graph theory and other recently developed transit network measures. The different transit modes included in the strategic plan are emphasized by adding weights to distinguish metro lines from light rail lines (LRT). This approach can help compare the combined metro or LRT alternatives of the new Tel Aviv plan to the metro-only alternatives as well as measure the performance relative to the metro systems in other cities around the world. The analysis of the alternative plans in Tel Aviv showed that when metro and LRT lines were treated as homogeneous modes, in which all were considered as metro, the alternatives resembled medium developed metro systems, such as in Barcelona and Washington DC. In contrast, when the distinguished weights were included, the combined metro/LRT alternatives resembled less developed systems, such as in Lyon and Lisbon, and only the metro-only alterative score remained high. The results also showed that the alternatives have regional coverage, and the alternatives with more LRT lines score lower in coverage. The network structure analysis showed that the metro-oriented networks score higher in both directness and connectivity. When using the weighted measures, the existing plan (LRT-only) scores low on both directness and connectivity. The analysis of the results emphasizes the need for more metro lines in the Tel Aviv metropolitan area. The results also suggest that the analysis of the complex mass transit networks based on graph theory should consider differences in line technology reflected in the line speed and coverage.

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Weighted complex network analysis of the Beijing subway system: Train and passenger flows

Cited by 55 publications

References 48 publications

Characterizing the Structure of the Railway Network in China: A Complex Weighted Network Approach

Characterizing the Structure of the Railway Network in China: A Complex Weighted Network Approach

Integrating network science and public transport accessibility analysis for comparative assessment

Network Analysis of the Tel Aviv Mass Transit Plan

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