Modeling and simulating traffic congestion propagation in connected vehicles driven by temporal and spatial preference

Liu, Zhao; Liu, Yanheng; Wang, Jian; Deng, Weiwen

doi:10.1007/s11276-015-1021-1

Cited by 13 publications

(4 citation statements)

References 45 publications

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“…The transfer rate from line 4 to line 2 differs greatly compared to the data obtained by Li [40], while the other transfer rates are similar. The collected data of the project evaluation report published by China Metro Engineering Consulting Company [41] are more accurate than the data obtained from Li.…”

Section: Data Preparationmentioning

confidence: 41%

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Zeng

2018

Urban Rail Transit

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Simulating the congestion propagation of urban rail transit system is challenging, especially under oversaturated conditions. This paper presents a congestion propagation model based on SIR (susceptible, infected, recovered) epidemic model for capturing the congestion prorogation process through formalizing the propagation by a congestion susceptibility recovery process. In addition, as congestion propagation is the key parameter in the congestion propagation model, a model for calculating congestion propagation rate is constructed. A gray system model is also introduced to quantify the propagation rate under the joint effect of six influential factors: passenger flow, train headway, passenger transfer convenience, time of congestion occurring, initial congested station and station capacity. A numerical example is used to illustrate the congestion propagation process and to demonstrate the improvements after taking corresponding measures.

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Section: Data Preparationmentioning

confidence: 41%

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Zeng

2018

Urban Rail Transit

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“…As shown in Fig. 1, we abstract a real road structure with m streets and n avenues into an m 9 n lattice [30], in which every intersection is transformed into a grid, and the four edges represent four directions numbered by 0, 1, 2, and 3, respectively. Every direction is composed of four lanes that are also numbered by 0, 1, 2, and 3.…”

Section: Vehicle Mobility Modeling 31 Road Topologymentioning

confidence: 99%

Vehicle mobility driven by traditional drivers versus connected drivers

Liu

Wang

et al. 2015

Wireless Netw

Self Cite

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The traditional vehicle mobility is restricted to the driver habits and the line-of-sight. The new-emerging connected vehicles enable information exchange with each other at vicinity, which will undoubtedly bring a greatly positive effect on the vehicle mobility pattern. In this paper, we propose to modeling and analyzing the vehicle mobility patterns respectively driven by traditional drivers and connected drivers. We perform the extensive simulations to explore the statistical differences between two mobility patterns and the underlying reasons through comparing to the real vehicle trace datasets. The results show that they behave quite diversely at the concerned aspects, e.g. degree distribution, clustering coefficient, topological shortest path, topological coefficient, and vehicle density, and also we find that the connected vehicles are well distributed and contribute a relieved traffic congestion situation.

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“…Research in urban traffic congestion: Traffic congestion occurs when a volume of traffic generates demand for space greater than the available street capacity [28]. The study of urban traffic congestion mainly involves the mechanism of congestion generation [29,30], the law of congestion propagation [31][32][33], the control of congestion dissipation [34] and the influence of urban spatial structure and land use pattern on urban traffic [35].…”

Section: Introductionmentioning

confidence: 99%

Urban Wasteful Transport and Its Estimation Methods

Cao

Zhou

Tang³

et al. 2018

Sustainability

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The paper studies urban road traffic problems from the perspective of resource science. The resource composition of urban road traffic system is analysed, and the road network is proved as a scarce resource in the system resource combination. According to the role of scarce resources, the decisive role of road capacity in urban traffic is inferred. Then the new academic viewpoint of “wasteful transport” was proposed. Through in-depth research, the paper defines the definition of wasteful transport and expounds its connotation. Through the flow-density relationship analysis of urban road traffic survey data, it is found that there is a clear boundary between normal and wasteful transport in urban traffic flow. On the basis of constructing the flow-density relationship model of road traffic, combined with investigation and analysis, the quantitative estimation method of wasteful transport is established. An empirical study on the traffic conditions of the Guoding section of Shanghai shows that there is wasteful transport and confirms the correctness of the wasteful transport theory and method. The research of urban wasteful transport also reveals that: (1) urban road traffic is not always effective; (2) traffic flow exceeding road capacity is wasteful transport, and traffic demand beyond the capacity of road capacity is an unreasonable demand for customers; (3) the explanation that the traffic congestion should apply the comprehensive theory of traffic engineering and resource economics; and (4) the wasteful transport theory and method may be one of the methods that can be applied to alleviate traffic congestion.

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Modeling and simulating traffic congestion propagation in connected vehicles driven by temporal and spatial preference

Cited by 13 publications

References 45 publications

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Vehicle mobility driven by traditional drivers versus connected drivers

Urban Wasteful Transport and Its Estimation Methods

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