Machine Learning-Based Traffic Flow Prediction Model

Abstract

This project aims to create a web application using Flask that forecasts traffic flow on roads by utilizing cutting-edge deep learning algorithms. The program uses a Multi-Layer Perceptron (MLP) regression model to estimate real-time traffic volume based on various input data, such as the date, time, temperature, and weather, taking holidays into consideration. The study thoroughly investigates data preparation procedures, including categorical variable encoding, feature extraction, and sorting. Moreover, it entails the MLP regression model's thorough implementation, which includes training, hyperparameter adjustment, and assessment. The careful execution of the traffic prediction model integration into the Flask framework allows for smooth application setup and interaction. This project aims to provide stakeholders and developers with a powerful tool for comprehending and controlling road traffic, with possible uses in urban planning and transportation management.