Predictive Traffic Modeling: A Machine Learning Approach to Time-Based Traffic Forecasting

Efficient traffic management plays a big role in decreasing congestion, thereby leading to a reduction of travel time and achieving maximum transport efficiency in an urban area with rapid urbanization and high vehicle density.This paper discusses a detailed study on time-based traffic prediction using state-of-the-art machine learning techniques.We discuss fusing different machine learning algorithms, namely linear regression, decision trees, and neural networks, to predict traffic flow from a time dimension.Historical traffic patterns, the weather, and other relevant variables are all considered so that dynamic, accurate predictive models can be developed.The performance of each machine learning model describes the temporal dynamics of traffic and, at the same time, provides thorough performance in terms of accuracy, computational efficiency, and scalability.We also further address the challenging issues of data preprocessing, choice of features, and optimization of models to enhance overall predictability.The results show that machine learning in traffic forecasting performs much better than traditional traffic forecasting methods in terms of giving better, more precise, and timely predictions that, when used by a traffic management system, optimize flows to reduce bottlenecks.This work contributes to the ever-growing field of Intelligent Transport Systems and acts as a stepping stone for further innovation in the domain of real-time traffic prediction and management.