@article{kafashan_niroumand_hajbabaie_2025, title={Optimal Connected Automated Vehicle Control in Freeway Merge Segments through Distributed Coordination}, volume={12}, url={https://doi.org/10.1177/03611981251384955}, DOI={10.1177/03611981251384955}, abstractNote={Recent advancements in connected automated vehicle (CAV) technologies promise significant improvements in traffic management, particularly in complex roadways such as freeway merge segments. However, achieving these improvements requires the implementation of a systematic control framework to coordinate CAV operations effectively. This paper presents a distributed cooperative optimization algorithm specifically designed to refine the trajectory and lane-changing decisions of CAVs. A vehicle-level mixed-integer nonlinear program is introduced, optimizing discrete lane-changing decisions and continuous lateral and longitudinal acceleration of CAVs. The optimization approach uses a hybrid solution technique that combines linearization with a receding horizon framework. This reduces computational complexity while ensuring adaptability to the traffic system’s dynamics. The algorithm is evaluated using a case study, and it significantly improves traffic flow efficiency. The results showed reductions of up to 93.6% in average delay, 50.0% in speed variation, and 47.6% in fuel consumption. Sensitivity analysis revealed the algorithm’s robustness across varying speed limits, demand levels, and lane configurations. For instance, while higher demand rates severely degrade traffic performance in simulation runs, the optimization consistently maintains low delays and high speeds. This shows the algorithm’s ability to adapt to challenging traffic conditions. In addition, sensitivity tests indicate that design features, such as longer acceleration lanes, reduce speed variations and improve merging efficiency. These results highlight the algorithm’s capability to deliver reliable and efficient traffic management under diverse operational scenarios.}, journal={Transportation Research Record Journal of the Transportation Research Board}, author={Kafashan, Fahim and Niroumand, Ramin and Hajbabaie, Ali}, year={2025}, month={Dec} }
 @article{niroumand_kafashan_hajibabai_hajbabaie_2025, title={Real‐time network‐level traffic signal and trajectory optimization with connected automated and human‐driven vehicles}, DOI={10.1111/mice.70139}, abstractNote={Abstract This paper introduces a real‐time framework designed to optimize intersection signal timing and vehicles’ trajectories across a network of intersections in a mixed environment of human‐driven and automated fleets. The network‐level optimization model is decomposed into intersection‐level sub‐models, whose decisions are coordinated through information exchange, aiming to push them toward the network model's optimal solutions. At each intersection, a bi‐level framework addresses both the signal timing and trajectory optimization models. A specialized greedy heuristic algorithm is developed for the lower‐level problem where optimal connected and automated vehicles (CAVs) trajectories are constructed for a given signal timing plan. At the upper level, all the feasible signal timing plans are created, and the system selects the most effective one to implement. The study integrates the entire solution process into a receding horizon framework to ensure efficient handling throughout the study period. A case study demonstrated the system's capability to adjust signals and trajectories effectively under various traffic demands and CAV market shares. Results showed a reduction in overall arterial delay correlating with higher proportions of CAVs. The proposed system delivered solutions in less than 70 ms, which is significantly faster than the half‐second solving time steps, ensuring decisions were made quicker than in real‐time.}, journal={Computer-Aided Civil and Infrastructure Engineering}, author={Niroumand, Ramin and Kafashan, Fahim and Hajibabai, Leila and Hajbabaie, Ali}, year={2025}, month={Nov} }