@article{islam_hajbabaie_2022, title={An Enhanced Cell Transmission Model for Multi-Class Signal Control}, volume={23}, ISSN={1524-9050 1558-0016}, url={http://dx.doi.org/10.1109/TITS.2021.3101838}, DOI={10.1109/TITS.2021.3101838}, abstractNote={Existing multi-class cell transmission model (CTM) based methodologies for signal timing or traffic assignment may transfer prioritized transit vehicles from one cell to the next one before processing their preceding passenger cars. In addition, existing CTM-based methodologies process a proportion of a slow-moving transit vehicle in each time step. As such a portion of each transit vehicle remains in each cell and it never clears them. This paper presents constraints to project the position of transit vehicles based on the speed and cell occupancy variations between different classes of vehicles and incorporates them into the CTM. The resulting optimization program is a mixed-integer nonlinear problem. We used a distributed receding horizon control framework to solve it in real-time. The proposed formulation is executed in a simulated arterial street with four signalized intersections in Springfield, IL with different traffic volume levels and transit vehicle frequencies. The results showed that the proposed algorithm addressed the mentioned issues of the existing multi-class CTM, and yielded more efficient network performance than the conventional transit signal priority-based (CTSP) systems. The proposed formulation reduced average bus delay by 1% to 70% and car delay by 52% to 76% compared to CTSP.}, number={8}, journal={IEEE Transactions on Intelligent Transportation Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Islam, S M A Bin Al and Hajbabaie, Ali}, year={2022}, month={Aug}, pages={11215–11226} }
 @article{bin al islam_abdul aziz_hajbabaie_2021, title={Stochastic Gradient-Based Optimal Signal Control With Energy Consumption Bounds}, volume={22}, ISSN={1524-9050 1558-0016}, url={http://dx.doi.org/10.1109/TITS.2020.2979384}, DOI={10.1109/TITS.2020.2979384}, abstractNote={This paper develops a stochastic gradient-based optimization model for traffic signal control with bounds on network-level vehicular energy consumption. The signal control problem is formulated as a mixed-integer linear mathematical program, which incorporates inequality constraints to limit the total energy consumption in the network. The developed stochastic gradient approximation algorithm provides a near-optimal solution to the non-convex optimization problem. To account for the energy consumption constraints, a penalty function method leveraging the pseudo gradient estimation technique is developed. Empirical results from a signalized arterial street show that it is possible to achieve optimized signal settings at the desired energy consumption bound without compromising delay. Further, we report the sensitivity of the energy bounds to the mobility metrics—system delay. Our novel gradient-approximation-based solution technique offers a functional and feasible way to accommodate non-convex energy consumption bounds within a signal control optimization model to achieve maximal mobility with minimal energy consumption.}, number={5}, journal={IEEE Transactions on Intelligent Transportation Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Bin Al Islam, S. M. A. and Abdul Aziz, H. M. and Hajbabaie, Ali}, year={2021}, month={May}, pages={3054–3067} }