Joint optimization of vehicle-group trajectory and signal timing: Introducing the white phase for mixed-autonomy traffic stream
Ramin Niroumand, Mehrdad Tajalli, Leila Hajibabai, Ali Hajbabaie
Abstract
This study develops a novel mixed-integer non-linear program to control the trajectory of mixed connected-automated vehicles (CAVs) and connected human-driven vehicles (CHVs) through signalized intersections. The trajectory of CAVs is continuously optimized via a central methodology, while a new “white” phase is introduced to enforce CHVs to follow their immediate front vehicle. The movement of CHVs is incorporated in the optimization framework utilizing a customized linear car-following model. During the white phase, CAVs lead groups of CHVs through an intersection. The proposed formulation determines the optimal signal indication for each lane-group in each time step. We have developed a receding horizon control framework to solve the problem. The case study results indicate that the proposed methodology successfully controls the mixed CAV-CHV traffic under various CAV market penetration rates and different demand levels. The results reveal that a higher CAV market penetration rate induces more frequent white phase indication compared to green-red signals. The proposed program reduces the total delay by 19.6%–96.2% compared to a fully-actuated signal control optimized by a state-of-practice traffic signal timing optimization software.