Optimizing operations at freeway weaves with connected and automated vehicles

In this research, we propose novel mathematical models and algorithms for optimizing connected and automated vehicles' (CAVs) trajectories at freeway weaving segments assuming 100 percent CAV market penetration. The proposed system receives vehicle arrival information and generates optimal trajectories that are relayed to the CAVs. The algorithm simulates vehicle arrivals and develops optimal vehicle trajectories on a 2-lane weaving section (one mainline and one auxiliary lane). We test the optimization algorithm under a variety of demand scenarios and using real-world arrival data. Results suggest that the proposed algorithm increases the average travel speed and capacity by 12 to 16% and up to 11%, respectively when compared to HCM estimates. Also, our case study shows the algorithm increases the average speed by 17%, 30%, and 38% for minimum time headways of 1.7 s, 1.4 s, and 1 s, respectively, compared to conventional vehicles.