Large Language Model Agents for Adaptive Traffic Signal Control: A Simulation Case Study in Nairobi

Abstract

Abstract Traffic congestion in Nairobi’s Central Business District continues to impose high economic, social, and environmental costs. Long queues at intersections, wasted fuel, and poor air quality are common outcomes of conventional fixed-time traffic signals. These systems dominate the city but do not respond to fluctuating or multimodal traffic. This study explored the use of Large Language Model (LLM) agents as adaptive controllers and compared their performance with existing fixed-time plans. Traffic video recordings were collected from selected intersections and analyzed using the YOLOv5 object detection algorithm to estimate lane-specific vehicle counts. The processed counts were then used to calibrate a Simulation of Urban Mobility (SUMO) environment. Within this setup, LLM agents allocated green times dynamically and adjusted signal phases in real time. The study adopted an experimental simulation design, testing both peak and off-peak traffic conditions as well as disruption scenarios such as blocked approaches and emergency vehicle passage. To ensure reliability, the SUMO model was calibrated against observed volumes and validated using standard traffic simulation statistics. Performance was assessed using three key indicators: average waiting time, intersection throughput, and responsiveness to demand fluctuations. Results showed that the LLM-based model reduced waiting times by up to 35%, increased throughput by 12–18%, and stabilized signal plans within fewer cycles than the fixed-time baseline. Beyond efficiency gains, the study demonstrates the feasibility of repurposing generalist AI models as decision agents in traffic management, offering a low-cost, scalable solution particularly suited to resource-constrained cities. By providing localized evidence from Nairobi, the research contributes to Intelligent Transportation Systems (ITS) literature and supports policy directions that include piloting AI-powered adaptive control at critical intersections as part of broader smart mobility strategies in African cities.