A Multi-class Lane-changing Advisory System for Freeway Merging Sections

Abstract

Cooperative intelligent transportation systems (C-ITS) support the exchange of information between vehicles and infrastructure (V2I or I2V). This paper presents an in-vehicle C-ITS application to improve traffic efficiency around a merging section. This application balances the distribution of traffic over the available lanes of a freeway, by issuing targeted lane-changing advice to a selection of vehicles. We add to existing research by embedding multiple vehicle classes in the lane-changing advisory framework. We use a multi-class multi-lane macroscopic traffic flow model to design a feedback-feedforward control law that is based on a linear quadratic regulator (LQR). The performance of the proposed system is evaluated using a microscopic traffic simulator. The results indicate that the lane-changing advisory system is able to suppress Shockwaves in traffic flow and can significantly alleviate congestion. Besides bringing substantial travel time benefits around merging sections of up to nearly 21%, the system dramatically reduces the variance of travel time losses in the system.