Feature Analysis on Mixed Traffic Flow of Manually Driven and Autonomous Vehicles Based on Cellular Automata

Hu, Xinghua; Huang, Mengyu; Guo, Jianpu

doi:10.1155/2020/7210547

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Their results showed that the critical vehicle density and the maximum average flow would increase significantly when the ratio of Avs to HVs increases. Hu et al [52] modeled mixed traffic based on an improved single-lane CA model that considered the dynamic headway and explored mixed traffic characteristics under different AV proportions. Considering the difference between CAVs, Avs, and HVs, Vranken et al [53] introduced a new CA model for single lane highways and set the timestep to 0.1 s to improve the model accuracy.…”

Section: Cellular Automata Model In Traffic Simulationmentioning

confidence: 99%

Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model

et al. 2022

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Mixed traffic composed of human-driven vehicles (HDVs) and CAVs will exist for an extended period before connected and autonomous vehicles (CAVs) are fully employed on the road. There is a consensus that dense fog can cause serious traffic accidents and reduce traffic efficiency. In order to enhance the safety, mobility, and efficiency of highway networks in adverse weather conditions, it is necessary to explore the characteristics of mixed traffic. Therefore, we develop a novel cellular automata model for mixed traffic considering the limited visual distance and exploring the influence of visibility levels and CAV market penetration on traffic efficiency. We design acceleration, deceleration, and randomization rules for different car-following scenes. For lane-changing, considering the interaction of CAVs and surrounding vehicles, we introduce game theory (GT) to lane-changing policies for CAVs. This paper presents the following main findings. In reduced visibility conditions, the introduction of CAVs is beneficial to improve mixed traffic efficiency on metrics such as free-flow speed and traffic capacity (e.g., 100% CAVs could increase the traffic capacity up to around 182% in environments of dense fog). In addition, the critical density increases as the proportion of CAVs increases, which is more pronounced in conditions of dense fog according to the simulation results. In addition, we compared the proposed GT-based lane-changing strategy to the traditional STCA lane-changing strategy. The results showed that the average speed is significantly improved under the proposed lane-changing strategy. The model presented in this paper can evaluate the overall performance and provide a reference for future management and control of mixed traffic flow in fog conditions.

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Section: Cellular Automata Model In Traffic Simulationmentioning

confidence: 99%

Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model

et al. 2022

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“…Meanwhile, some countries have carried out pilot operations of intelligent connected vehicles [11]. During the development of CAV, the road traffic flow will present a mixed scenario of manually driven vehicles and intelligent networked vehicles, and there are different control measures for the traffic flow operation scenarios under different circumstances [12][13]. Unlike manually driven vehicles, intelligent networked vehicles can realize the information interaction between vehicles and vehicles and between vehicles and roads and other infrastructures with the help of vehicle networking technology [14][15].…”

Section: Introductionmentioning

confidence: 99%

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

Zhao,

Wen,

Wang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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This paper analyzes the intelligent networked fleet formation mechanism under adaptive cruise control (CACC). By constructing a communication architecture system including network layer, coordination layer, control layer and physical layer, and based on environment sensing technology and 5G network, a layered control structure for autonomous driving vehicles is established. In the study, the inter-vehicle distance control strategy is proposed, and combined with the theory of metacellular automata, the study constructs an intelligent networked fleet model, which adopts the full speed difference model and adaptive cruise control vehicle following model. The simulation analysis is carried out through the actual data of a section of G6 Beijing-Tibet Expressway, and the simulation environment is a two-lane highway of 3000m, considering different CAV vehicle penetration rates (0% to 100%). The results show that with the increase of CAV penetration, the traffic flow and the congestion significantly improved. The intelligent Connected Vehicles fleet performs well in the obstacle avoidance task, effectively reducing unnecessary lane changes and shortening the congestion time compared to the conventional system, with the distance error controlled within 1m. This study shows that the intelligent connected vehicles-connected fleet has significant advantages in improving traffic flow’s operational efficiency and safety.

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“…With the model’s simplicity, high computational efficiency, and capability to capture the characteristics of real traffic, it is also applied in other complex scenarios like work zone ( 19–21 ) and pedestrian flow ( 22–24 ). Recently, CA models ( 25–31 ) have been used to simulate the heterogeneous traffic of human-driven vehicles and CACC vehicles. Ye and Yamamoto ( 25 ) constructed a CA model for both human driving vehicles and CACC vehicles and evaluated the impact of CACC vehicles on traffic safety.…”

mentioning

confidence: 99%

Impact of Cooperative Adaptive Cruise Control on a Multilane Highway under a Differentiated Per-Lane Speed Limit Policy

Guo

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Cooperative adaptive cruise control (CACC) has drawn wide attention in recent years for its potential throughput benefit, as it is a promising intermediate technology to the highly connected and automated vehicles. The impact of CACC on multilane highways has been the subject of several studies, but they assumed traffic under a uniform speed limit. Recent research has revealed that traffic performs differently under a differentiated per-lane speed limit (DPLSL) policy with heavy vehicle (HV) restricted lanes. Whether the benefits of CACC still remain under a DPLSL policy has not been explored. This study developed cellular automaton models to incorporate CACC-equipped and non-equipped vehicles (i.e., passenger cars, HVs) on a two-way eight-lane highway with a DPLSL. Results shown throughputs by lane increase up to 78.5% as the CACC car market penetration rate (MPR) rises. Such increases became sharper (i.e., ≥10%) for inner lanes (i.e., HV restricted lanes) and outer lanes after reaching a 40% and a 60% CACC car MPR, separately. Moreover, HVs induced a 1.5% to 15.7% throughput reduction across lanes even under higher CACC car MPRs (i.e., 60%, 80%). This DPLSL policy may cause the lanes to experience a throughput penalty when they are adjacent to lanes with a different speed limit as the MPR of CACC cars rises. Lastly, in traffic with a 60% CACC car MPR, increases are brought further by considering 10% of HV with CACC, especially on those HV non-restricted lanes. The study is helpful for policy makers to further prepare for the prevalence of CACC in the forthcoming years.

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Feature Analysis on Mixed Traffic Flow of Manually Driven and Autonomous Vehicles Based on Cellular Automata

Cited by 7 publications

References 23 publications

Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model

Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

Impact of Cooperative Adaptive Cruise Control on a Multilane Highway under a Differentiated Per-Lane Speed Limit Policy

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