Connected automated vehicles orchestrating human-driven vehicles: Optimizing traffic speed and density in urban networks

Connected automated vehicles (CAVs) have untapped potential to regulate mixed traffic by orchestrating the movement of human-driven vehicles (HVs) at intersections. This research introduces a new controller role for CAVs as regulators of mixed traffic in connected environments. Coordinating the movement of HVs by synchronizing the speed and alignment of CAVs acting as platoon leaders at intersections is a stochastic process with state transition probabilities that vary with traffic speed and vehicular density at the network level. We tackle the problem of regulating mixed traffic at intersections at a macroscopic scale and develop a stochastic model to enhance the operation of mixed traffic consisting of HVs and CAVs by optimizing traffic speed and vehicular density at the network level. Traffic speed and vehicular density are interdependent and vary together at the network level. Therefore, we employ the concept of the network Macroscopic Fundamental Diagram (MFD) to optimize vehicular density by adjusting the spacing between vehicle platoons, led by CAVs, to maximize intersection capacity and network flow at a larger scale. The proposed model is premised on a first-in-first-out reservation-based approach developed for coordinating the movement of vehicle platoons across multiple lanes moving together in cohorts, led by CAVs, at intersections. We account for the randomness in the size, alignment, and arrival time of platoons at intersections in heterogeneous traffic conditions and develop a Markovian approach to capture the stochasticity in modeling the coordination process at intersections. We capture the interrelationship between traffic speed, vehicular density, and inter-cohort spacing at the network level and estimate the upper bound of the flow as a function of density under different CAV penetration rate scenarios. Our numerical results show that optimizing traffic speed and density by adjusting the average spacing between platoons led by CAVs, when the CAV penetration rate in mixed traffic is as low as 20%, can increase the network flow up to 54% of the maximum capacity achievable under uniform CAV traffic conditions.