An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection

The application of connected automated vehicles (CAVs) provides new opportunities and challenges for optimizing and controlling urban intersections. To avoid collisions of vehicles in conflicting directions at intersections and improve the efficiency of intersections, an optimal scheduling model for CAVs at an unsignalized intersection is proposed. The model develops a linear programming model of intersection vehicle timing with the minimum average vehicle delay within the optimization time window as the optimization objective and the minimum safe time interval for vehicles to pass through the intersection as the constraint. A rolling optimization algorithm is designed to improve the efficiency of the algorithm solution. Finally, the effects of different traffic demand conditions on the results are investigated based on numerical simulation experiments. The results show that both the proposed algorithm and the Gurobi solver can significantly reduce the average vehicle delay compared with the first-come-first-served (FCFS) control method, and the proposed model and algorithm can reduce the average vehicle delay by 76.22% at most. Compared with the Gurobi solver, the proposed model and algorithm can reduce the solution time and ensure the optimization effect to the greatest extent. Therefore, the proposed model and algorithm provide theoretical support for managing CAVs at unsignalized intersections.