Connected automated vehicles (CAVs) are typically equipped with communication devices (e.g., dedicated short range communications (DSRC)) and on-board sensors (e.g., Radar, Lidar, Camera, etc.). Communication devices would enable the exchange of real-time information between vehicles and infrastructures via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) channels. Sensors equipped in vehicles are providing various vehicle sensor data (VSD) such as the CAV’s GPS location, speed and moving direction (trajectory). Existing studies have shown the effectiveness of using CAV trajectories as input in many traffic control models.
However, it can be expected that CAVs and human-driven vehicles (HVs) will co-exist on the transportation network in a long period. Hence, to support various traffic control tasks, it is critical to develop a reliable model to understand the real-time traffic pattern in a mixed CAV and HV environment. To satisfy such needs, this project firstly introduces a novel macroscopic traffic flow model which treats CAVs and HVs as separate groups, where a new set of factors are introduced to represent the speed change of HVs due to following CAVs in the traffic stream. Then grounded on the traffic flow model, an optimization function will make real-time adjustment of CAVs’ desired speeds for minimizing the total freeway travel delays.
Selecting a segment of I-15 in Salt Lake City as the study site, the research team conducts extensive simulation experiments to evaluate highway performance under different demand levels and CAV penetration rates. Corresponding data analysis reveals that there should exist a critical CAV ratio that can greatly reduce the speed difference between CAVs and HVs in the traffic stream, given the demand pattern. Further discussions have highlighted the need of setting up guidance on highway capacity analysis under the CAV environment.