Development of an Intelligent Traffic Management System Using Machine Learning Techniques

Abstract

Urban traffic congestion produces recurrent delay, fuel loss, emissions, and unreliable travel times, while conventional fixed-time signal plans cannot continuously adapt to rapidly changing demand. This methodology paper presents the development framework for an intelligent traffic management system that combines real-time traffic sensing, machine-learning-based traffic state prediction, and reinforcement-learning-based signal control. The proposed architecture receives vehicle counts, speed, occupancy, queue length, and time-of-day variables; cleans and aggregates the data; predicts near-term traffic conditions using supervised models such as Random Forest and Long Short-Term Memory networks; and selects signal phases through a Deep Q-Network controller. The system is designed to be evaluated in a microscopic traffic simulator using fixed-time and actuated control as baselines. Performance is measured through average delay, queue length, throughput, travel time, prediction error, and decision latency. The literature up to 2020 shows that deep learning improves nonlinear traffic prediction and that reinforcement learning can adapt signal timing at both isolated and network levels. An illustrative simulation-stage evaluation indicates that the proposed integrated approach can reduce delay and queue formation compared with conventional control while maintaining feasible real-time computation. The paper emphasizes reproducibility, safe deployment through simulation-before-field testing, and interpretable performance indicators rather than uncontrolled online learning on live roads.