Research of vehicle flow based on cellular automaton in different safety parameters

Jiang, Hui; Zhang, Zhenpei; Huang, Qiuyan; Xie, Pengyang

doi:10.1016/j.ssci.2015.09.020

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Later, models with more complex algorithms appeared, for example, allowing us to reflect the three-phase theory of Kerner [16]. Nowadays, the models of this type remain one of the principal directions of transport modeling [9,[31][32][33][34].…”

Section: State Of the Art In The Research Fieldmentioning

confidence: 99%

Development of Parallel Algorithms for Intelligent Transportation Systems

et al. 2022

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This paper deals with the creation of parallel algorithms implementing macro-and microscopic traffic flow models on modern supercomputers. High-performance computing contributes to the development of intelligent transportation systems based on information technologies and aimed at the effective regulation of traffic in large cities. As a macroscopic approach, the quasi-gas-dynamic traffic model approximated by explicit finite-difference schemes is proposed. One- and two-dimensional variants of the system are considered, and the concept of lateral velocity and different equations for obtaining it are discussed. The microscopic approach is represented by the multilane cellular automata model. The previously developed model is extended to reproduce synchronized flow in accordance with Kerner’s three-phase theory. The new version starts from the Kerner–Klenov–Schreckenberg–Wolf model and operates with the concept of the synchronization gap. Macroscopic models are relevant for determining the common characteristics of road traffic, while microscopic models are useful for a detailed description of cars’ movement. Both approaches possess inner parallelism. The parallel algorithms are based on the geometrical parallelism principle with different boundary conditions at interfaces of the subdomains. Sufficiently high speedups were reached when up to 100 processors were involved in calculations. The proposed algorithms can serve as the core of ITS.

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Section: State Of the Art In The Research Fieldmentioning

confidence: 99%

Development of Parallel Algorithms for Intelligent Transportation Systems

et al. 2022

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“…The cellular automata (CA) approach has a long and successful history of usage in various fields of research, including medical, biological and social sciences. It was introduced into the traffic field by K. Nagel and M. Schreckenberg in 1992 [2] and, since then, is widely used for modelling vehicular traffic all over the world [3][4][5][6][7][8][9].…”

Section: Cellular Automata (Ca) Model For Traffic Flowsmentioning

confidence: 99%

Driver Behaviour Algorithms for the Cellular Automata-Based Mathematical Model of Traffic Flows

2021

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The work is devoted to the development of new algorithms describing the complex interaction of drivers for the parallel numerical implementation of the traffic flow model developed by the authors based on the cellular automata theory. As is known from observations, the behaviour of drivers largely determines how difficult it will be to pass a section with a bottleneck (a narrowing, a motionless obstacle, etc.) or even without it at the same flow values. In such conditions, cars can move in a synchronized flow at a more or less constant speed or go into “stop-and-go” mode. The authors have developed a set of algorithms for a large number of different road situations, which is featured in this article.

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“…Microscopic models depict the underlying process of individual driver acceleration behaviour based on the velocity and headway information of vehicles using car following models [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and Cellular automata (CA) [23][24][25], while macroscopic models are represented by hydrodynamic continuum equations [26][27][28][29][30][31][32] to reflect aggregate characteristics of traffic flow (e.g. velocity, density, and flow).…”

Section: Introductionmentioning

confidence: 99%

A viscous continuum traffic flow model based on the cooperative car‐following behaviour of connected and autonomous vehicles

Jafaripournimchahi

Cai

Wang

et al. 2022

IET Intelligent Trans Sys

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Connected and Autonomous Vehicles (CAVs) can receive various information from surrounding vehicles through Vehicle‐to‐Everything (V2X) communication technologies and adjust their car‐following behaviour accordingly. Although several studies have evaluated the impact of CAVs on traffic flow stability in a small segment of networks, most approaches are focused on their specific applications considering the trajectory information, and there is a lack of studies analyzing the impact of CAVs on a large‐scale network. This paper proposes a novel viscous continuum traffic model considering the anticipation of space headway, the throttle angle, and brake torque information during cooperative car‐following. The methods employed to develop the new car‐following model and its counterpart continuum traffic model have been described. The linear and non‐linear stability analyses of the newly developed model have been conducted to obtain the critical stability factors in small perturbations. Numerical simulations have been carried out to investigate the effect of the anticipation, the throttle angle, and brake torque information on traffic stability, fuel consumption, and exhaust emissions. The numerical results reveal that the anticipation of space headway and the transmission of the throttle angle and brake torque information during cooperative car‐following manoeuvres can improve the traffic flow stability and reduce fuel consumption and emissions.

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Research of vehicle flow based on cellular automaton in different safety parameters

Cited by 10 publications

References 14 publications

Development of Parallel Algorithms for Intelligent Transportation Systems

Development of Parallel Algorithms for Intelligent Transportation Systems

Driver Behaviour Algorithms for the Cellular Automata-Based Mathematical Model of Traffic Flows

A viscous continuum traffic flow model based on the cooperative car‐following behaviour of connected and autonomous vehicles

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