A novel congestion propagation modeling algorithm for smart cities

Nagy, Attila Matyas; Simon, Vilmos

doi:10.1016/j.pmcj.2021.101387

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…In this work, we limit our discussion to speed and congestion indices, however, it is noteworthy that other indices such as travel rate, delay, and level of services are also valid and frequently used measures for quantifying the level of congestion [28][29][30].…”

Section: Traffic Congestionmentioning

confidence: 99%

Public Transport Congestion Detection Using Incremental Learning

Makara¹,

Maric²,

Pekár

2022

SSRN Journal

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Section: Traffic Congestionmentioning

confidence: 99%

Public Transport Congestion Detection Using Incremental Learning

Makara¹,

Maric²,

Pekár

2022

SSRN Journal

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“…Saberi et alapplied a transmission model of infectious disease spread in a population to a traffic congestion model and validated its effectiveness using large-scale data from six cities worldwide [29]. The congestion propagation modeling algorithm proposed by Nagy and Simon was the first algorithm to determine the propagation time expectation and the propagation probability of any propagation pattern using Markov chains [30].…”

Section: Introductionmentioning

confidence: 99%

Propagation and control of congestion risk in scale-free networks based on information entropy

Yan,

Li,

Sun

et al. 2024

PLoS ONE

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To study the propagation pattern of congestion risk in the traffic network and enhance risk control capabilities, a model has been developed. This model takes into account the probabilities of five threats (the risk occurrence probability; the risk of loss; the unpredictability of risk; the uncontrollability of risk; the transferability of risk) in the traffic network to define the risk entropy and determine the risk capacity, analyze the mechanism of congestion risk propagation, and explore the impact of risk resistance, the average degree of risk capacity at intersections, and the degree of correlation on congestion risk propagation. Further, a control method model for risk propagation is proposed. Numerical simulation results demonstrate that the risk resistance parameter θ can inhibit the propagation of congestion risk during traffic congestion. The highest efficiency in controlling risk propagation is achieved when θ reaches a threshold value θ*. Furthermore, the average degree of intersection risk capacity α shows a positive correlation with θ* and a negative correlation with control efficiency. However, the degree of association ω has a negative effect on risk propagation control, decreasing the degree of association between nodes aids in risk propagation control.

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“…Many scholars have studied the propagation of the crowd in transportation networks. Nagy and Simon predicted the congestion propagation by Markoc chains; the approach accurately predicted the probability of congestion occurring many times per day and the duration of congestion propagation periods [5]. Shang et al estimated the passenger flow state by integrated Lagrangian and Eulerian observations [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Crowded Propagation on the Metro Network

et al. 2022

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The crowd in a metro system can cause inconvenience and even safety problems to passengers. The study of crowded propagation in metro systems can identify where and when crowds occur, ensuring travel quality and safety. Based on this, a modified susceptible–infected-susceptible (SIS) crowded propagation model is proposed to estimate the risk probability of crowding (RPC) in the metro network. Each station’s real transport capacity is considered. Infection rate and the recovery rate are proposed considering the traffic difference between stations. Using the Beijing metro network as a case study, the spatial and temporal patterns of crowded propagation are analyzed, and the types of nodes suitable for regulation are further discussed. This proposed model can provide a reference for RPC identification and regulation and promote sustainable development of metro operations.

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A novel congestion propagation modeling algorithm for smart cities

Cited by 13 publications

References 14 publications

Public Transport Congestion Detection Using Incremental Learning

Public Transport Congestion Detection Using Incremental Learning

Propagation and control of congestion risk in scale-free networks based on information entropy

Analysis of Crowded Propagation on the Metro Network

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