Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China

Du, Zhouyang; Tang, Jinjun; Qi, Yong; Wang, Yiwei; Han, Chunyang; Yang, Yifan

doi:10.1016/j.physa.2019.122926

Cited by 62 publications

(33 citation statements)

References 38 publications

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“…Taking both node centrality and traffic flow into consideration, Xu et al [9] introduced a data-driven framework to identify the key nodes with the use of comprehensive vehicle trajectories and geographic information. e authors in [10] proposed a method named the improved topological potential model considering entropy, which took both centrality and invulnerability into consideration to evaluate the key nodes of the metro network.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

Wang

Dong

Shao

et al. 2021

Journal of Advanced Transportation

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This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

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Section: Literature Reviewmentioning

confidence: 99%

Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

Wang

Dong

Shao

et al. 2021

Journal of Advanced Transportation

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“…Moreover, a series of models including the capacity-load model [17][18][19], binary influence model [20,21], sandpile model [22], ORNL-PSerc-Alaska (OPA) [23], CASCADE model [24], and coupled map lattice model [25], has been proposed to describe the cascading failure phenomenon of the URTN. The current research mainly focuses on the capacity-load model, which assumes that the capacity is proportional to the initial load and is mainly applied to simulate the cascading failure of the network [26].…”

Section: Introductionmentioning

confidence: 99%

Vulnerability Assessment of the Urban Rail Transit Network Based on Travel Behavior Analysis

Liu

Zhu

et al. 2021

IEEE Access

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“…As the connections of different metro lines, transfer stations are crucial in metro networks. Some researchers utilized the complex network theory to investigate the characteristics of the metro networks such as Beijing [ 3 ], Shanghai [ 4 ], Guangzhou [ 5 ], and some other cities [ 6 ]. The findings of their studies indicated that transfer stations played the most significant role in the networks.…”

Section: Introductionmentioning

confidence: 99%

“…), the AFC data has been used in the researches of transportation engineering. These studies are mainly focused on four fields: prediction of passenger flow [ 2 , 7 , 10 , 11 , 12 ], analysis of passenger flow patterns [ 13 ], investigation of passenger behaviors [ 14 , 15 ], and evaluation of metro networks [ 3 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Zhou

Wang

Zhao

2020

Sensors

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The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting.

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Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China

Cited by 62 publications

References 38 publications

Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

Vulnerability Assessment of the Urban Rail Transit Network Based on Travel Behavior Analysis

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

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