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

Abstract

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.

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

“…The authors use a Stackelberg game model to capture the interactions between vehicles, where a leader vehicle set the rules for other followers to follow. To further enhance the effectiveness of game-theoretic methods, Gong et al propose a game-theoretic reinforcement learning approach for CAVs, where vehicles learn to cooperate to maximize the overall system performance [261]. However, it is still computationally expensive and challenging to implement in practice.…”

The effect of visibility on road traffic during foggy weather conditions

“…They showed that new drivers drive at higher speeds and exhibit less hazard avoidance compared to experienced drivers. In [9], a mixed traffic model was developed to improve the safety, mobility, and efficiency of highway networks in foggy weather conditions. It was shown that the average velocity and road capacity decrease when visibility is reduced.…”

“…It has been shown that the rate of traffic accidents is 35 times higher in fog compared to clear weather [8]. Adverse weather conditions contribute to approximately 20% of traffic accidents and cause 38.3% of traffic congestion, 23% of non-recurring delays, and billions of dollars in losses [9].…”

“…Drivers obtain almost 90% of their traffic information through vision, and fog can significantly reduce their ability to perceive the surrounding environment [9]. The reduced visibility conditions caused by fog result in velocity reduction and increased delay.…”

Research on the Deployment of Joint Dedicated Lanes for CAVs and Buses

“…The entry of intelligent CAVs into urban transport networks and the competition with traditional transport modes for limited road resources, and how to maximize the use of road resources to improve the overall efficiency of the transport system, are key issues in transport planning in the connected environment [12,13]. At the same time, a long transition period is needed for the replacement of non-connected vehicles by CAVs, and traffic planning must be based on the reality of long-term coexistence of heterogeneous traffic streams [14,15]. The installation of dedicated lanes for CAVs can provide a separate operating environment for CAVs, but it is a large investment and takes up a lot of existing road resources.…”