@article{mehrabipour_hajbabaie_2022, title={A Distributed Gradient Approach for System Optimal Dynamic Traffic Assignment}, volume={23}, ISSN={1524-9050 1558-0016}, url={http://dx.doi.org/10.1109/TITS.2022.3163369}, DOI={10.1109/TITS.2022.3163369}, abstractNote={This study presents a distributed gradient-based approach to solve system optimal dynamic traffic assignment (SODTA) formulated based on the cell transmission model. The algorithm distributes SODTA into local sub-problems, who find optimal values for their decision variables within an intersection. Each sub-problem communicates with its immediate neighbors to reach a consensus on the values of common decision variables. A sub-problem receives proposed values for common decision variables from all adjacent sub-problems and incorporates them into its own offered values by weighted averaging and enforcing a gradient step to minimize its objective function. Then, the updated values are projected onto the feasible region of the sub-problems. The algorithm finds high quality solutions in all tested scenarios with a finite number of iterations. The algorithm is tested on a case study network under different demand levels and finds solutions with at most a 5% optimality gap.}, number={10}, journal={IEEE Transactions on Intelligent Transportation Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Mehrabipour, Mehrzad and Hajbabaie, Ali}, year={2022}, month={Oct}, pages={17410–17424} }
 @article{tajalli_mehrabipour_hajbabaie_2021, title={Network-Level Coordinated Speed Optimization and Traffic Light Control for Connected and Automated Vehicles}, volume={22}, ISSN={1524-9050 1558-0016}, url={http://dx.doi.org/10.1109/TITS.2020.2994468}, DOI={10.1109/TITS.2020.2994468}, abstractNote={This study develops a methodology for coordinated speed optimization and traffic light control in urban street networks. We assume that all vehicles are connected and automated. The signal controllers collect vehicle data through vehicle to infrastructure communications and find optimal signal timing parameters and vehicle speeds to maximize network throughput while harmonizing speeds. Connected and automated vehicles receive these dynamically assigned speeds, accept them, and implement them. The problem is formulated as a mixed-integer non-linear program and accounts for the trade-offs between maximizing the network throughput and minimizing speed variations in the network to improve the network operational performance and at the same time smoothen the traffic flow by harmonizing the speed and reducing the number of stops at signalized intersections. A distributed optimization scheme is developed to reduce the computational complexity of the proposed program, and effective coordination ensures near-optimality of the solutions. The case study results show that the proposed algorithm works in real-time and provides near-optimal solutions with a maximum optimality gap of 5.4%. The proposed algorithm is implemented in Vissim. The results show that coordinated signal timing and speed optimization improved network performance in comparison with cases that either signal timing parameters or average speed of vehicles are optimized. The coordinated approach reduced the travel time, average delay, average number of stops, and average delay at stops by 1.9%, 5.3%, 28.5%, and 5.4%, respectively compared to the case that only signal timing parameters are optimized.}, number={11}, journal={IEEE Transactions on Intelligent Transportation Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Tajalli, Mehrdad and Mehrabipour, Mehrzad and Hajbabaie, Ali}, year={2021}, month={Nov}, pages={6748–6759} }