A Vehicle Control Model to Alleviate Traffic Instability.

While bringing convenience to people, the growing number of vehicles already on roads causes inevitable traffic congestion. Traffic congestion can happen for both observable reasons, such as accidents, or occur naturally due to human driving style in high vehicles density scenarios. In the latter case, traffic congestion are referred to as phantom jams. To alleviate the traffic instability caused by phantom jams, bilateral control models have been proposed with the development of intelligent transportation system (ITS) in ideal situations. However, in road scenarios, some factors that severely affect the performance of existing models cannot be ignored, such as uncertainties of vehicle state measurements caused by on-board sensors, time delay caused by intervehicle communications and control systems of vehicles. In this paper, a novel predictable bilateral control model (PBCM) with local wireless communication, which consists of best estimation and state prediction, is proposed to accurately control the host vehicle in traffic flow to alleviate traffic instability. Compared with other models, theoretical analysis and simulation results show that more accurate control decisions could be obtained with the PBCM, which could distribute all vehicles more evenly, and our model could maintain higher velocities when disturbances occurred. The experimental results also reveal that our proposed model could more effectively suppress traffic instability (i.e., the improvement in control decision $a$ is more than 10%) and could restore traffic balance 20% faster than other models on average. [ABSTRACT FROM AUTHOR]