A cooperative driving framework for urban arterials in mixed traffic conditions

Enabling technologies of connected and automated vehicles (CAVs) bring new opportunities to signalized intersection control. CAVs not only provide a new source of data for traffic management but also can be controlled as actuators to improve traffic flow. This study proposes a hierarchical and implementation-ready cooperative driving framework with a mixed traffic composition of CAVs, connected vehicles (CVs), and regular vehicles (RVs) for urban arterials. The proposed framework combines centralized and distributed control concepts, where the infrastructure generates optimal signal timing plans and provides high-level trajectory guidance to the CAVs while detailed trajectories are generated by each vehicle. The system consists of three levels of models. At the vehicle level, a state transition diagram is designed for different modes of operations of CAVs including eco-trajectory planning, cooperative adaptive cruise control (CACC) and collision avoidance. At the intersection level, a mixed-integer linear programming (MILP) problem is formulated to optimize the signal timing plan and arrival time of CAVs, with consideration of CACC platooning behaviors. At the corridor level, link performance functions are applied to calculate the total delay of the coordinated phases of each intersection, and a linear programming (LP) problem is formulated to optimize the offsets for every cycle, which are then passed to the intersection level. Simulation results from a calibrated real-world arterial corridor show that both mobility and fuel economy benefits from the cooperative driving framework. The total delay is reduced by 2.2%−33.0% and fuel consumption by 3.9%−7.4%, with different mixture of vehicle compositions and CAV penetration rates (e.g., 0%−100%). Sensitivity analysis on volume fluctuation is performed, which confirms the benefits of the dynamic offset optimization.


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  • Accession Number: 01767701
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 14 2021 3:42PM