Spatial Agent-Based Simulation of Connected and Autonomous Vehicles to Assess Impacts on Traffic Conditions.

Traffic congestion and its effect on aging transportation infrastructures have been a significant issue in many cities. Various policies such as fast-track lanes have been applied to optimize traffic on roadways. However, the increasing adoption of Connected and Autonomous Vehicles (CAVs) motivates the question of whether they can reduce traffic congestion. This study aims to evaluate the integration of CAVs into existing transportation networks, comprising of both highway and urban roads. To quantify their impact, we develop and validate agent-based simulation models. Two study sites in the State of Oklahoma were identified. We then implemented connected cruise control, green light optimized speed advisory, dynamic route selection, and pedestrians' detection as behaviors for CAVs in the simulation. The results indicated that introducing CAVs to the selected road networks improved the traffic flow by more than 30% and 20% of the average travel time for the urban and highway study sites, respectively.