Cellular Automata–Based Modeling and Simulation of a Mixed Traffic Flow of Manual and Automated Vehicles

Yang, Da; Li, Xiaoping; Ma, Lina; Wu, Danhong; Zhu, Lei; Liang, Hongbin

doi:10.3141/2622-10

Cited by 30 publications

(9 citation statements)

References 24 publications

(29 reference statements)

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“…Accordingly, there are many studies on CAVs using the CA traffic simulation model. To reflect different driving logics of different types of vehicles, different CA update rules were adopted for HDVs and CAVs [18][19][20]. Researchers have modeled mixed traffic flow with CA models by considering some important factors such as drivers' character differences, aggression levels of AVs, differentiated per-lane speed limit policy, and dedicated lane policy [21][22][23][24][25].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather

Hou

2023

Sustainability

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Connected and autonomous vehicles (CAVs) are expected to significantly improve traffic efficiency and safety. However, the overall impacts of CAVs on mixed traffic have not been clearly studied because most previous research focused on one subset of the performance of mixed traffic. This study aims to provide complete information for the policymakers to make better decisions on future CAV implementation strategies with a comprehensive evaluation of the overall performance of mixed traffic. With this purpose, this study develops an integrated framework to evaluate the efficiency and safety of mixed traffic with CAVs under adverse weather conditions, which is composed of a traffic simulation, multi-vehicle crash model, single-vehicle crash model, and performance assessment. For the first time, a unified performance index is introduced to reflect the overall efficiency and safety performance of mixed traffic. The proposed framework is demonstrated with an evaluation of the performance of mixed traffic on a highway segment. Traffic efficiency and safety under different weather conditions are investigated. The impact of reaction time of human-driving vehicles (HDVs) and CAVs are also studied. Simulation results show that the overall traffic performance in terms of traffic efficiency, multi-vehicle safety, and single-vehicle safety increases with the increase in the market penetration rate (MPR). In addition, it is found that CAVs have a greater impact on improving overall traffic performance under rainy and snowy weather than in clear weather. Moreover, a shorter reaction time of HDVs and CAVs can lead to better overall traffic performance.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather

Hou

2023

Sustainability

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“…For the study of multi-lane traffic flow modeling, scholars mainly focus on the lanechanging rules. Based on the safe distance rule proposed by Gipps [54], Yang et al [55] developed a new lane change rule for mixed traffic. However, Gipps' lane-changing model ignores the differences of drivers, especially under different traffic conditions.…”

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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“…Many studies have been done on the characteristics of the heterogeneous flow consisting of AVs and HDVs on freeways [4], [5], and at intersections [6], [7]. However, in the context of AVs, few studies have been conducted focusing on bus stops, which are also a crucial component in urban road networks.…”

Section: Introductionmentioning

confidence: 99%

Influence of Bus Bay on Heterogeneous Traffic Flow Consisting of Human Driving and Autonomous Vehicles

Zhou,

Liu,

Liu

et al. 2023

IEEE Access

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With the deployment of autonomous vehicles (AV), it can be assumed that a heterogeneous flow consisting of human driving and autonomous vehicles will appear in urban road networks. Numerous studies have shown that bus stops tend to have great influence on the nearby traffic flow, but few insightful researches have been conducted on the influence of bus bays in the context of heterogeneous traffic flow consisting of human driving vehicles and autonomous vehicles. In this paper, a new KKSW (Kerner-Klenov-Schreckeneberg-Wolf)-BB (bus bay)-AVHDV (autonomous vehicles and human driving vehicles) CA (cellular automata) model is proposed to study the influence of bus bays on heterogeneous traffic flow based on the well-known KKSW CA model. Through numerical simulations, the flow characteristics, congestion patterns and speed characteristics in the heterogeneous traffic flow are studied. The frequency and severity of rear-end collisions are analyzed to study the influence of the bus bay on traffic safety. The results show that bus bays may have a significant impact on traffic flow efficiency and traffic safety. The introduction of AVs in the heterogeneous traffic flow could significantly improve traffic efficiency and safety near the bus bay, especially under the condition of heavy traffic flow.INDEX TERMS Bus bay, bus interference, three-phase traffic theory, cellular automata model, heterogeneous traffic flow, human driving and autonomous vehicles, traffic conflict techniques

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Cellular Automata–Based Modeling and Simulation of a Mixed Traffic Flow of Manual and Automated Vehicles

Cited by 30 publications

References 24 publications

Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather

Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather

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

Influence of Bus Bay on Heterogeneous Traffic Flow Consisting of Human Driving and Autonomous Vehicles

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