Ant Colony Optimization With Look-Ahead Mechanism for Dynamic Traffic Signal Control of IoV Systems

Abstract

In the era of Internet of Vehicle (IoV), traffic data are obtained by wireless sensor networks (WSNs), which provides convenience for real-time traffic signal control and is expected to further improve the efficiency of the whole system. Traffic signal controllers designed for IoV systems usually fully depend on data transmitted from the IoV system and are limited to the penetration rate of connected vehicles. This work proposes a dynamic ant colony optimization algorithm with the look-ahead mechanism (DALM) with eight no-conflict phases. The needed traffic information in the DALM algorithm is small and can be obtained independently by the system, which makes this method does not fully rely on the vehicle trajectories data and can better protect customer privacy and deal with cyber attack. With the look-ahead mechanism, the forthcoming traffic flow together with the current waiting queue are involved to predict the length of waiting vehicles and the optimal phase sequence is dynamically selected according to shortest green-light duration of candidate phase sequences obtained form the collected real-time traffic data. Experiments conducted under five scenarios with different kinds of traffic flow on SUMO platform show that compared with other four methods, both the total waiting time and the total carbon emissions of vehicles at the intersection are greatly reduced by using the DALM algorithm, especially in high traffic flow situations. The proposed algorithm can greatly improve the efficiency of the intersection in high traffic flow situation and is predictable to benefit the real-time traffic signal control in IoV system.