Abstract
Autonomous intersection management (AIM) will be a future method for improving traffic efficiency in the urban area. Instead of using the traffic signal control like nowadays, it uses wireless communication with autonomous vehicles to support the management of road traffic more safely and efficiently. A single AIM shows an exceptional performance in managing traffics at an intersection. However, it could not be represented a traffic in the real world, which is composed of multiple intersections. We show that coordination of traffic information among vehicles and infrastructures is an essential part of macroscopic traffic management. Coordination of traffic information among the network of AIMs is the key to improve the overall traffic flow throughout the network not only has an optimal flow in some intersections and very heavy traffic in others. In this paper, we introduce the distributed control to a graph-based intersection network to control traffic in a macroscopic level. Vehicle to infrastructure and infrastructure to infrastructure communication are used to exchange the traffic information between a single autonomous vehicle to the network of autonomous intersections. We implement a discrete time consensus algorithm to coordinate the traffic density of an intersection with its neighborhoods and determine the control policy to maximize a traffic throughput of each intersection as well as stabilizing the overall traffic in the network. We use the Greenshields traffic model to define the boundary condition of various traffic flows to the corresponded traffic density and velocity. Our proposed method represents the ability to maintain traffic flow rate of each intersection without having a back up traffic. As well, every intersection operates under the uncongested flow condition. The simulation results of the graph-based networked control of a multiple autonomous intersection showed that the overall traffic flow in the network achieves up to 20% higher than using traffic signal system.
Highlights
Traffic signal is the general method to manage vehicles crossing an intersection
The simulation results of the graph-based networked control of a multiple autonomous intersection showed that the overall traffic flow in the network achieves up to 20% higher than using traffic signal system
We present the simulation results of multiple autonomous intersection managements, which were implemented, based on the discrete consensus algorithm with the Greenshields traffic model
Summary
Traffic signal is the general method to manage vehicles crossing an intersection. We can say that this method can totally prevent an accident when all drivers strictly follow the signal and traffic rule. Greenshields model defined the relationship between traffic velocity and traffic flow rate with parabolic function as refer to the stability of the street network since the congested traffic or traffic jamming represents the instability. Algorithm 1 AIM: Consensus coordination of the local traffic information define: i: Observed intersection j: The neighborhood intersection k: Discrete time update l: The external street m: Numbers of vehicle on a street in the range of V2I communication n: Numbers of street connected to an intersection t: Continuous time update procedure Main((ai j , di j ∈ lai j ), ci j ) Si ← Initialization while not Si is empty do. We present the simulation results of multiple autonomous intersection managements, which were implemented, based on the discrete consensus algorithm with the Greenshields traffic model. The comparison plot of three traffic parameters between consensus-based AIM and the traditional traffic light signal system is shown in Figs. 16, 17 and 18
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