Traffic flow guidance in mixed traffic via eco-lane-changing control of connected and automated vehicles using signal phase and timing information

With the advancement of intelligent transportation technology, the utilization of connected and automated vehicles (CAVs) to guide traffic flow has attracted considerable research attention. This paper addresses the issue of CAVs guiding traffic flow by adjusting their motion (speed adjustment and lane change) based on the signal light information and surrounding vehicle status. To the best of our knowledge, the model of traffic flow guidance via CAV lane-changing behavior is rarely reported. In this paper, a unidirectional dual-lane model with mixed traffic flow is considered. In particular, CAVs collect position and speed data of nearby human-driven vehicles (HDVs) through vehicle-to-vehicle (V2V) communication and receive signal phase and timing (SPaT) information through vehicle-to-infrastructure (V2I) communication under a distance-triggered mechanism. We develop an algorithm to calculate the desired speed to ensure the platoon passes the traffic light during its green light period (GLP). With the desired speed, the CAV adjusts its motion (speed adjustment and lane change) to guide the HDV platoon in the target lane passing the traffic light during GLP. Additionally, we develop a constrained iterative LQR (CILQR)-based algorithm to optimize CAV energy consumption during the lane-changing process. Finally, simulation results illustrate the effectiveness of our method. Compared to scenarios without CAV guidance, the average speed of the HDV platoon increased by 64.97%, and the time to pass through the traffic signal was reduced by 53.25%. Our proposed model and method reduced the overall energy consumption of the entire traffic system by 38.13%.