Influence of Lane Policies on Freeway Traffic Mixed with Manual and Connected and Autonomous Vehicles

Abstract

Connected and autonomous vehicles (CAVs) have become the highlights of traffic. Researchers in this field have proposed various traffic management measures to enhance the capacity and efficiency of traffic with CAVs, especially mixed traffic of CAVs and manual vehicles (MVs). Exclusive lane setting is included. However, exclusive lane policy-related researches for mixed traffic of CAVs and MVs were very limited, and the influence of number and location of exclusive lanes on the mixed traffic was unclear. To fill this gap, this paper aims to study the influence of different exclusive lane policies on mixed traffic and provide recommended lane policies under various traffic volumes and CAV penetration rates. Freeways with two lanes and three lanes in a single direction were taken into consideration, and sixteen lane policies were proposed. Then different lane policies were simulated with a new proposed cellular automata (CA) model, and properties including flux, average speed, and CAVs degradation were analyzed to evaluate the traffic efficiency of each lane policy. The results show that CAV exclusive lanes can improve the capacity, while MV exclusive lanes seem helpless for capacity improvement. Seven lane policies, including GC, GM, and CM for two-lane freeways and GCG, CGC, and CCM for three-lane freeways, outperform the others in terms of average speed. In addition, exclusive lanes can reduce the probability that CAVs degenerate to AVs. Our findings may help to optimize freeways’ lane policies and improve the efficiency of heterogeneous traffic mixed with CAVs and MVs.

Stability Analysis of Heterogeneous Traffic Flow with Connected and Automated Vehicles: Joint Consideration of Communication Failures and Driver Takeover

“…In recent years, intelligence agents with connection characteristics have attracted much attention, especially in the field of connected and automated vehicles (CAVs). CAVs can take rational dynamics automatically by perceiving information with intervehicle communications [1,2], including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, fully mature CAVs, which meet Level 5 in the Society of Automotive Engineers (SAE) J3016, are not available because of the current limitations on communication technologies [3].…”

Overall Influence of Dedicated Lanes for Connected and Autonomous Vehicles on Freeway Heterogeneous Traffic Flow

“…Zhang et al [44] studied the influence of CAV dedicated lanes on the freeway heterogeneous traffic flow from the perspective of traffic safety. Hua et al [45] studied the influence of different dedicated lane strategies on the heterogeneous traffic flow. e results showed that the CAV dedicated lanes could improve the freeway capacity, but dedicated lanes for MVs had little impact on the improvement of the freeway capacity.…”

Stacking‐based ensemble learning method for cognitive distraction state recognition for drivers in traditional and connected environments

“…In addition, with the development of 5G technology for mobile communication and intelligent technology, intelligent vehicles are rapidly developing. Soon, a mixture of various types of intelligent vehicles will become normal [9], and manual driving vehicles (MVs), connected manual vehicles [10,11] (CMVs), automated vehicles [12,13] (AVs), and connected automated vehicles [14][15][16] (CAVs) will simultaneously share the road [17][18][19]; moreover, the driving environment will become complex and changeable, and there will be an increasing number of factors that could induce cognitive distracted driving behaviour. Cognitive distraction will seriously affect driving safety [20], so such distraction in a connected environment is an important research topic addressed in this paper.…”