Network-Level Coordinated Speed Optimization and Traffic Light Control for Connected and Automated Vehicles

Abstract

This study develops a methodology for coordinated speed optimization and traffic light control in urban street networks. We assume that all vehicles are connected and automated. The signal controllers collect vehicle data through vehicle to infrastructure communications and find optimal signal timing parameters and vehicle speeds to maximize network throughput while harmonizing speeds. Connected and automated vehicles receive these dynamically assigned speeds, accept them, and implement them. The problem is formulated as a mixed-integer non-linear program and accounts for the trade-offs between maximizing the network throughput and minimizing speed variations in the network to improve the network operational performance and at the same time smoothen the traffic flow by harmonizing the speed and reducing the number of stops at signalized intersections. A distributed optimization scheme is developed to reduce the computational complexity of the proposed program, and effective coordination ensures nearoptimality of the solutions. The case study results show that the proposed algorithm works in real-time and provides nearoptimal solutions with a maximum optimality gap of 5.4%. The proposed algorithm is implemented in Vissim. The results show that coordinated signal timing and speed optimization improved network performance in comparison with cases that either signal timing parameters or average speed of vehicles are optimized. The coordinated approach reduced the travel time, average delay, average number of stops, and average delay at stops by 1.9%, 5.3%, 28.5%, and 5.4%, respectively compared to the case that only signal timing parameters are optimized.

Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

“…The modeling and analysis of mixed traffic flow is an emerging research field, in which partial solutions have been achieved. Modeling methods with classical model-based approaches do exist [11], as do those with unconventional, e.g., network-level [12] or data-driven approaches [20]. Methodologically, the classical traffic modeling methods are based on physical relationships, in which the nonlinear characteristics of the traffic flow are described.…”

“…Control-oriented applications are strongly connected to the modeling of traffic flow. For example, in [12], a network-level coordination for automated vehicle control and traffic light control was presented, i.e., a distributed optimization scheme to reduce the computational complexity and to improve the effectiveness of coordination was developed. A more complex problem was that of control in mixed traffic, because the motion of automated vehicles and the motion of human-driven vehicles simultaneously impact the traffic flow.…”

Effects of Autonomous Driving Behavior on Intersection Performance and Safety in the Presence of White Phase for Mixed-Autonomy Traffic Stream

“…Feeding signal timing plans to CAV trajectory optimization programs can improve intersection performance measures (23)(24)(25). Intersection performance measures can be further enhanced by jointly optimizing the signal timing plans and CAV trajectories in fully automated environments (26)(27)(28)(29)(30)(31). Although joint signal and trajectory optimization shows substantial improvements in intersection performance measures, it may not lead to utilizing the maximum capacity of intersections in fully connected and automated environments (32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42).…”

Coordination of Lateral Vehicle Control Systems Using Learning-Based Strategies

“…The coordination can be carried out on the level of network, e.g., in [18] a coordination strategy is applied for public transport services. Another hot topic is the coordination of vehicles and transportation systems in intersection scenarios, where the effectiveness of the operation through optimal coordination of interventions can be achieved [19,20].…”