Ai algorithm for predictive analysis of highway traffic incidents

Abstract

This invention relates to an artificial intelligence-based predictive traffic management system designed to forecast highway traffic incidents, including traffic jams, accidents, floods, landslides, and other disruptions. The system leverages a hybrid machine learning architecture comprising Long Short-Term Memory (LSTM) neural networks for time-series forecasting, Random Forest classifiers for incident detection, Convolutional Neural Networks (CNN) for video analysis, and Reinforcement Learning (RL) for adaptive decision-making. By integrating real-time data from IoT sensors, traffic cameras, weather APIs, and historical traffic records, the algorithm preprocesses and normalizes data to ensure accurate predictions. The ensemble learning approach combines outputs from these models to provide actionable insights such as traffic rerouting, emergency response activation, and maintenance planning. Additionally, the system incorporates a continuous learning feedback loop to refine model performance based on evolving traffic and environmental patterns. This innovative solution enhances highway safety, reduces congestion, and improves operational efficiency through proactive traffic management and decision support. Drawing accompanying abstract: Figure 1