Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Navrati Saxena
Second Advisor
Amith Kamath Belman
Third Advisor
Pooja Shyamsundar
Keywords
Adaptive Traffic Signal Control, Mixture-of-Experts, Genetic Algo- rithm, Static and Dynamic Gating, Real-Time Control, Multi-Objective Optimization.
Abstract
Urban traffic signal control often needs to juggle between competing goals. It needs to minimize delays, reduce emissions, prevent crashes, and prioritize emergency vehicles all while the demand is constantly fluctuating. Traditional fixed-time or statically blended policies cannot reallocate priorities quickly when conditions change. We introduce GeneGate, a mixture-of-experts framework that uses a lightweight genetic gate to fuse four specialist controllers (throughput, emissions, safety, emergency) and adjusts their weights in real time. A short offline genetic search produces a robust initial blend, and an online micro-evolution step refines it every few cycles based on live traffic feedback. GeneGate’s adaptive gating enables to detect temporal shifts in live traffic such as recognizing the intersection to be collision prone area, or emergency vehicle prone area and so on, without the need for manual retuning. In simulation on a single section testbed, GeneGate outperforms fixed time, maximum pressure, actuated baselines and a unified neural expert, delivering high reductions in collisions, travel time, and CO2 emissions, along with faster emergency response times. Furthermore, this genetic gating approach is fully general and can be applied to other dynamic environments requiring real-time, multi-objective fusion.
Recommended Citation
Bhat, Rashmi Vishwanath, "GeneGate: Genetic Gating in a Mixture-of-Experts for Real-Time Multi-Objective Traffic Signal Control" (2025). Master's Projects. 1571.
DOI: https://doi.org/10.31979/etd.j5uw-qh7g
https://scholarworks.sjsu.edu/etd_projects/1571
