Optimizing Freeway Merge Operations under Conventional and Automated Vehicle Traffic

Abstract

This paper presents an optimization algorithm for freeway operations at merge zones that maximizes the average speed of the segment in the presence of connected and automated vehicles (CAVs) and human-operated (i.e., conventional) vehicles. This research assumes that CAVs have the capability to communicate with each other and with the infrastructure and to execute the recommended trajectories. The proposed system receives arrival information as input and generates optimal trajectories for CAVs while predicting the behavior of conventional vehicles and accounting for deviation from expected behavior. The necessary algorithms are developed to simulate and carry out the merging operations on a two-lane freeway (one mainline and one ramp lane) and tested under a variety of scenarios considering demand level, demand splits, and CAV penetration rate. Results suggest that the proposed algorithm can efficiently manage the traffic at freeway merge zones and reduce the average total travel time (or increase average speed). The results indicate that a minimum of 25% CAV penetration rate is required to observe improvements in operational conditions.