The UTC Graduate School is pleased to announce that Vijayalakshmi Karattuppalayam Kumarasamy will present Doctoral research titled, Decentralize Graph-based Multi-Agent Reinforcement Learning for Traffic Signal Optimization on 10\/10\/2025 at 10:AM in MDRB Conference Room. Everyone is invited to attend.
Computational Science
Chair: Yu Liang
Co-Chair: Dalie Wu
Abstract:
Signalized intersections are persistent bottlenecks where inefficient operations contribute to congestion, delays, safety risks, and environmental impacts. Conventional control strategies provide stability under predictable demand but lack the adaptability required to manage stochastic and heterogeneous traffic conditions. This dissertation develops a decentralized graph-based multi-agent reinforcement learning (DGMARL) framework for adaptive traffic signal control. The framework advances the state of the art by (i) embedding operational constraints, including minimum\/maximum green durations, pedestrian recalls, and clearance intervals, directly into the learning process, (ii) modeling intersections as decentralized agents that exchange direction-specific states through multi-head graph attention to capture asymmetric flows and upstream inflows, thereby enabling scalable coordination across large networks, and (iii) incorporating contextual pedestrian demand via point-of-interest weighting. Control policies are optimized within a constrained Markov decision process, where modular phase selection and fairness-aware rewards jointly balance vehicle efficiency and pedestrian accessibility. The framework is validated using a high-fidelity digital twin\u2013based simulator with real-world traffic data and further demonstrated through preliminary field integration. Across evaluations on the MLK Smart Corridor, the proposed approach reduced pedestrian waiting times by up to 24.7% and vehicle delays by 22.6%, while decreasing emissions (CO, CO_2, NO_x, PM_10) by an average of 9.6% and increasing vehicle throughput by more than 22%. These improvements were achieved while ensuring compliance with safety-critical signal timing rules. Analysis of graph attention weights highlights interpretable coordination across intersections, confirming the robustness and scalability of the decentralized design under varied traffic conditions. Together, these contributions establish a pathway toward deployment-ready, equitable, and sustainable traffic signal control across diverse network settings.<\/p>\n","protected":false},"excerpt":{"rendered":"
The UTC Graduate School is pleased to announce that Vijayalakshmi Karattuppalayam Kumarasamy will present Doctoral research titled, Decentralize Graph-based Multi-Agent Reinforcement Learning for Traffic Signal Optimization on 10\/10\/2025 at 10:AM in MDRB Conference Room. Everyone is invited to attend. Computational Science Chair: Yu Liang Co-Chair: Dalie Wu Abstract: Signalized intersections are persistent bottlenecks where inefficient operations contribute to congestion, delays, safety risks,… <\/p>\n