A mobility network approach to identify and anticipate large crowd gatherings

Huang, Zhiren; Wang, Pu; Zhang, Fan; Gao, Jianxi; Schich, Maximilian

doi:10.1016/j.trb.2018.05.016

Cited by 56 publications

(44 citation statements)

References 40 publications

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“…Under anomalous traffic conditions, passenger demands may exceed the maximum capability that a subway station can provide; emergent managements are therefore required to protect the safety and order of subway transportation. In addition, under large crowd gatherings, subway service restrictions can be an important way to prevent passengers from flowing into the crowded area, hence avoiding dangerous crowding situations [52]. Therefore, predicting passenger flows under anomalous traffic conditions is even more important than predicting flow under ordinary conditions.…”

Section: Resultsmentioning

confidence: 99%

Predicting subway passenger flows under different traffic conditions

et al. 2018

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Passenger flow prediction is important for the operation, management, efficiency, and reliability of urban rail transit (subway) system. Here, we employ the large-scale subway smartcard data of Shenzhen, a major city of China, to predict dynamical passenger flows in the subway network. Four classical predictive models: historical average model, multilayer perceptron neural network model, support vector regression model, and gradient boosted regression trees model, were analyzed. Ordinary and anomalous traffic conditions were identified for each subway station by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The prediction accuracy of each predictive model was analyzed under ordinary and anomalous traffic conditions to explore the high-performance condition (ordinary traffic condition or anomalous traffic condition) of different predictive models. In addition, we studied how long in advance that passenger flows can be accurately predicted by each predictive model. Our finding highlights the importance of selecting proper models to improve the accuracy of passenger flow prediction, and that inherent patterns of passenger flows are more prominently influencing the accuracy of prediction.

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Section: Resultsmentioning

confidence: 99%

Predicting subway passenger flows under different traffic conditions

et al. 2018

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“…Network interruption or degraded performance occurs when the traffic flow in some areas or edges in the network exceeds its own capacity constraints and is incapable of spreading to other parts of the network [2,32,33]. In other words, measuring the relationship between nodes, as well as the connection information in the local area, is the key to examining the networks performance.…”

Section: Related Workmentioning

confidence: 99%

Measuring road network topology vulnerability by Ricci curvature

Gao

Xing-quan

Liu

et al. 2019

Physica A: Statistical Mechanics and its Applications

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Describing the basic properties of road network systems, such as their robustness, vulnerability, and reliability, has been a very important research topic in the field of urban transportation. Current research mainly uses several statistical indicators of complex networks to analyze the road network systems. However, these methods are essentially node-based. These node-based methods are more concerned with the number of connections between nodes, and lack of consideration for interactions. So, this leads to the well-known node paradox problem, and their ability of characterizing the local and intrinsic properties of a network is weak. From the perspective of network intrinsic geometry, this paper proposes a method for measuring road network vulnerability using a discrete Ricci curvature, which can identify the key sections of a road network and indicate its fragile elements. The results show that our method performs better than complex network statistics on measuring the vulnerability of a road network. Additionally, it can characterize the * Corresponding author evolution of the road network vulnerability among different periods of time in the same city through our method. Finally, we compare our method with the previous method of centrality and show the different between them. This article provides a new perspective on a geometry to analyze the vulnerability of a road network and describes the inherent nature of the vulnerability of a road system from a new perspective. It also contributes to enriching the analytical methods of complex road networks.

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“…Lee et al used GTFS data to investigate the symmetry of boarding and alighting in time and space [36]. Huang et al combined GTFS data and taxi GPS data to identify and anticipate large crowd gatherings [37].…”

Section: Introductionmentioning

confidence: 99%

Understanding coupling dynamics of public transportation networks

et al. 2018

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Subway and bus networks work as an integrated multiplex transportation system and play an indispensable role in modern big cities. Even though a variety of works have investigated the coupling dynamics of multiplex transportation networks, empirical data that validates the determinant coupling factors are still lacking. In this paper, we employ smartcard data of 2.4 million subway and bus passengers in Shenzhen, China to study the coupling dynamics of subway and bus networks. Surprisingly, the coupling of subway and bus networks is not notably influenced by the time-varying speed ratio of the two network layers but is jointly determined by the distribution of travel demands and transportation facilities. Our findings highlight the important role of real travel demand data in analyzing the coupling dynamics of multiplex transportation networks. They also suggest that the speed ratio of different network layers, which was regarded as a key factor in determining coupling strength, has a negligible effect on travelers' route selections, and thus the coupling dynamics of multiplex transportation networks.

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A mobility network approach to identify and anticipate large crowd gatherings

Cited by 56 publications

References 40 publications

Predicting subway passenger flows under different traffic conditions

Predicting subway passenger flows under different traffic conditions

Measuring road network topology vulnerability by Ricci curvature

Understanding coupling dynamics of public transportation networks

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