A Vehicles Control Model to Alleviate Traffic Instability

TL;DR

This paper proposes a novel vehicle control model that uses estimation and prediction to reduce traffic instability caused by phantom jams, accounting for uncertainties and delays in real-world scenarios.

Contribution

Introduction of the Predictable Bilateral Control Model (PBCM) that improves traffic flow stability by mitigating measurement errors and communication delays.

Findings

PBCM effectively reduces traffic instability in simulations.

The model improves accuracy of vehicle state control under uncertainties.

Simulation results confirm the model's robustness against delays.

Abstract

While bringing convenience to people, the growing number of vehicles on road already cause inevitable traffic congestion. Some traffic congestion happen with observable reasons, but others occur without apparent reasons or bottlenecks, which referred to as phantom jams, are caused by traditional vehicle following model. In order to alleviate the traffic instability caused by phantom jam, several models have been proposed with the development of intelligent transportation system (ITS). these have been proved to be able to suppress traffic instability in the ideal situation. But in road scenarios, uncertainties of vehicle state measurements and time delay caused by on-board sensors, inter-vehicle communications and control system of vehicles will affect the performance of the existing models severely, and cannot be ignored. In this paper, a novel predictable bilateral control model-PBCM, which consists of best estimation and state prediction is proposed to determine accurate acceleration values of the host vehicle in traffic flow to alleviate traffic instability. Theoretical analysis and simulation results show that our model could reduce the influence of the measurement errors and the delay caused by communication and control system effectively, control the state of the vehicles in traffic flow accurately, thus achieve the goal of restrain the instability of traffic flow.