An asymmetric cellular automata model for heterogeneous traffic flow on freeways with a climbing lane

Liu, Yang; Zheng, Jianlong; Cheng, Yang

doi:10.1016/j.physa.2019.122277

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…In this situation, modeling the driver behavior of HDVs and the accuracy of car-following model of HDVs in dense fog environment will directly affect the performance of the fleet management, such as feasibility, reliability, stability, and robustness. e cellular automata (CA) model is widely used in traffic flow analysis and has advantages in describing the complex behavior and simulating the characteristics of traffic flow under various scenarios and traffic conditions [25][26][27][28]. However, as the CA models in the previous researches mainly focused on interactive between homogeneous vehicles, the current car-following model based on CA cannot be used directly between heterogeneous vehicles environment.…”

Section: Model Formulation 221 E State Space Model Of Cavsmentioning

confidence: 99%

“…en, u 1 � max(u min , u v1 ) according to formula (33). Based on formulas (1), (22), (27), (30), and (31), the range of u msd2 can be obtained as…”

Section: Is Between the Two Roots Of Formula 30mentioning

confidence: 99%

“…In the aspect of dense fog in the highway, the previous researches mostly focused on the association between the driver behavior and traffic accidents by considering the driver's physiology and psychology and environmental factors [12,23,24]. is paper focuses on modeling the driver behavior in the fleet mixed with HDVs and CAVs based on the Nagel-Schreckenberg (NaScH) cellular automatic (CA) model, which has advantages in describing the complex behavior and simulating the characteristics of traffic flow under various scenarios and traffic conditions [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment

Gong

Wei

et al. 2020

Journal of Advanced Transportation

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Adverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highway, this paper uses a mathematical modeling method to study and control the fleet mixed with human-driven vehicles (HDVs) and connected automatic vehicles (CAVs) in dense fog environment on highway based on distributed model predictive control algorithm (DMPC), along with considering the car-following behavior of HDVs driver based on cellular automatic (CA) model. It aims to provide a feasible solution for controlling the mixed flow of HDVs and CAVs more safely, accurately, and stably and then potentially to improve the mobility and efficiency of highway networks in adverse weather conditions, especially in dense fog environment. This paper explores the modeling framework of the fleet management for HDVs and CAVs, including the state space model of CAVs, the car-following model of HDVs, distributed model predictive control for the fleet, and the fleet stability analysis. The state space model is proposed to identify the status of the feet in the global state. The car-following model is proposed to simulate the driver behavior in the fleet in local. The DMPC-based model is proposed to optimize rolling of the fleet. Finally, this paper used the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment. Finally, numerical experiments were performed in MATLAB to verify the effectiveness of the proposed model. The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability.

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Section: Model Formulation 221 E State Space Model Of Cavsmentioning

confidence: 99%

“…en, u 1 � max(u min , u v1 ) according to formula (33). Based on formulas (1), (22), (27), (30), and (31), the range of u msd2 can be obtained as…”

Section: Is Between the Two Roots Of Formula 30mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment

Gong

Wei

et al. 2020

Journal of Advanced Transportation

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“…As the above traffic situation is becoming more and more serious, scholars have advanced many traffic models to analyze traffic behaviors and solve traffic congestion from different perspectives. Then, there have occurred the car-following models, [1][2][3][4][5][6][7][8][9] cellular automata models, [10][11][12][13][14][15] and lattice hydrodynamic models. [16][17][18][19][20][21][22] Among them, the optimal velocity (OV) model [3] is a classic car-following model.…”

Section: Introductionmentioning

confidence: 99%

A novel car-following model by sharing cooperative information transmission delayed effect under V2X environment and its additional energy consumption

et al. 2022

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A novel car-following model is offered based on the cooperative information transmission delayed effect involving headway and velocity under V2X environment. The stability conditions and mKdV equation of the new model are obtained via the linear and nonlinear analysis. Through numerical simulation, the variation trend of headway and hysteresis phenomenon are researched. At the same time, we investigated the additional energy consumption of the vehicle during acceleration. In brief, theoretical analysis and simulation results confirm that the new car-following model based on the cooperative information transmission delayed effect can improve traffic stability and reduce additional energy consumption.

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“…Methodologically, traffic flow models can be grouped into two categories: microscopic models and macroscopic models. The former group is represented by car following models [5][6][7][8][9][10] and cellular automaton models, [11][12][13] while the latter is represented by continuous flow models [14][15][16][17][18][19] and lattice hydrodynamics models. [20][21][22][23][24][25][26][27] Different to microscopic models, the macroscopic models take the traffic flow as a compressible continuous fluid composed of a number of vehicles, of which the simulation time is indifferent to the number of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous traffic flow modeling with drivers’ timid and aggressive characteristics*

Zhang

Luo

2021

Chinese Phys. B

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The driver's characteristics (e.g., timid and aggressive) has been proven to greatly affect the traffic flow performance, whereas the underlying assumption in most of the existing studies is that all drivers are homogeneous. In the real traffic environment, the drivers are distinct due to a variety of factors such as personality characteristics. To better reflect the reality, we introduce the penetration rate to describe the degree of drivers' heterogeneity (i.e., timid and aggressive), and proceed to propose a generalized heterogeneous car-following model with different driver's characteristics. Through the linear stability analysis, the stability conditions of the proposed heterogeneous traffic flow model are obtained based on the perturbation method. The impacts of the penetration rate of drivers with low intensity, the average value and standard deviation of intensity parameters characterizing two types of drivers on the stability of traffic flow are analyzed by simulation. Results show that higher penetration of aggressive drivers contributes to traffic flow stability. The average value has a great impact on the stability of traffic flow, whereas the impact of the standard deviation is trivial.

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An asymmetric cellular automata model for heterogeneous traffic flow on freeways with a climbing lane

Cited by 15 publications

References 25 publications

Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment

Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment

A novel car-following model by sharing cooperative information transmission delayed effect under V2X environment and its additional energy consumption

Heterogeneous traffic flow modeling with drivers’ timid and aggressive characteristics*

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