Optimization Study On The Deployment Of Dedicated Autonomous Driving Lanes And Traffic Control On Urban Roads

As autonomous driving technology gradually becomes popular,the introduction of autonomous vehicles(AV)will change the traditional traffic,and mixed traffic flow will bring new challenges to traffic management.Implementing dedicated right-of-way management can fully leverage the characteristics of AVs and optimize the overall traffic performance.This study focuses on the isolated intersection and urban network scenarios,targeting different AV penetration rate conditions.Based on traffic optimization and related technologies,a joint optimization model for dedicated AV lane deployment,flow distribution,and signal control is developed.At the same time,vehicle-level right-ofway allocation methods are explored for future scenarios with fully autonomous driving,and efficient space-time routing strategies for AVs are researched using technologies such as deep reinforcement learning to assist traffic managers in making scientific decisions.The primary contributions and novel ideas of this study are as follows.1.Study the joint optimization of dedicated AV lanes deployment,flow distribution and signal control at isolated intersections under mixed autonomy.The study first proposes an optimal signal control problem based on a given lane configuration and flow assignment.Then the theoretical characteristics of the optimal lane-level flow assignment and optimal lane configuration problems are derived.After that,the joint optimization of dedicated AV lane configuration,flow distribution and signal control is constructed as a mixed integer nonlinear programming problem,and a heuristic algorithm is proposed to solve the problem effectively based on the theoretical features.Numerical examples verify the effectiveness of the algorithm and analyze the conditions of AV penetration that enable the advantage of dedicated AV lanes under different numbers of lanes in one approach.2.Study the joint optimization of dedicated AV lanes deployment,flow distribution and signal control under mixed traffic network.The approach integrates the joint optimization problem into the network traffic assignment framework and systematically evaluates the impact of dedicated AV lanes on traffic delays resulting from additional mandatory lane changes.The study decomposes the network-level joint optimization problem into two sub-problems: network-level traffic assignment and intersection-level joint optimization.Alternating iterations are performed until convergence is achieved.The numerical results confirms that the distinct path selection strategies of autonomous and manually driven vehicles lead to different traffic allocations in the network.Consequently,the benefits of assigning dedicated lanes for autonomous driving are more pronounced in the road network.3.Study the vehicle-level right-of-way allocation and optimization methods for urban networks in fully automated driving scenarios.This part of the research establishes a conflict point network and proposes two space-time routing algorithms for a future scenario with 100% autonomous driving penetration.The research develops a ”platoon strategy” to organize autonomous vehicles to form a platoon through conflict points,and uses deep reinforcement learning to dynamically optimize the platoon size.Numerical tests show that the algorithm can achieve a balance between solution quality and computational load,guaranteeing real-time solutions with delays close to 0 in low-demand scenarios,and that the platoon strategy can significantly reduce the average vehicle delay under congestion in high-demand scenarios.The aforementioned three parts provide a systematic study of the management and control methods for the introduction of AVs into traditional traffic.This paper discusses in depth the advantages of setting up dedicated lanes for autonomous driving under a variety of traffic scenarios and conditions,and proposes a complete set of analysis methods to support urban road traffic management decisions.