Relative Fairness Signal Optimization Considering Traffic Benefits for Different Types of Vehicles

As autonomous driving technology advances, future intersections will witness the coexistence of two distinct vehicle categories: connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). Since different types of vehicles bring varying benefits to the transportation system, assigning the same right-of-way to different types of vehicles would result in unfairness in the transportation system. To align the traffic priority of different types of vehicles with their benefits to the transportation system, this study proposes a signal optimization model that takes into account the relative fairness factors and is solved using an improved particle swarm algorithm. On the one hand, this study introduces an archive module to improve the uniformity and stability of the improved algorithm results. On the other hand, this study incorporates a decision-maker preference module to improve the accuracy of the improved algorithm for the preference region. Finally, the Simulation of Urban Mobility (SUMO) software was utilized to conduct experiments under two conditions: the same permeability of each arm traffic flow and the different permeability of each arm traffic flow. The results indicate that the proposed optimization method is capable of refining the signal control strategy with the traffic benefits conferred by different types of vehicles. Under the different arm permeability set in this study, the proposed signal optimization method can reduce the average delay time for CAVs by 23.86%, thereby facilitating the deployment of CAVs.