Machine Learning-Based Traffic Flow Prediction for Enhanced Traffic Management

Abstract

Accurate traffic flow prediction is crucial for effective traffic management strategies, enabling dynamic signal control, congestion mitigation, and improved transportation efficiency. Traditional traffic prediction methods often rely on historical data and fixed traffic patterns, failing to capture the dynamic nature of traffic flow and its susceptibility to real-time events and incidents. In recent years, machine learning (ML) has emerged as a powerful tool for traffic flow prediction, offering the potential to analyse complex traffic data and identify patterns that traditional methods may overlook. This paper presents a novel approach to traffic flow prediction using ML techniques, aiming to enhance traffic management and reduce congestion in urban areas. The proposed approach employs a combination of ML algorithms, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, to capture the temporal dependencies and long-range relationships within traffic data. The ML models are trained on a comprehensive dataset of historical traffic data, incorporating various factors such as traffic volume, vehicle speed, and weather conditions. The trained ML models can then predict future traffic flow patterns based on real-time traffic data collected from sensors embedded in the road infrastructure.