Traffic congestion Level Prediction using Temporal, weather and Event Based Features

Project Code :TCMAPY1983

Objective

The objective of the project is to develop a machine learning-based system that predicts traffic congestion levels (HIGH, MEDIUM, LOW) using temporal, weather, and event-related data.

Abstract

This project focuses on predicting traffic congestion levels using machine learning models based on temporal, weather, and event-based features. By leveraging data on factors such as time of day, weather conditions (temperature, humidity, wind speed), local events, and holidays, the system aims to classify traffic conditions as "HIGH", "MEDIUM", or "LOW". The project utilizes algorithms such as XGBoost and Random Forest for accurate predictions and stores the results in a database for easy access. The system offers a user-friendly interface where predictions can be made through a web-based application. This tool provides valuable insights for urban planning, traffic management, and route optimization, ultimately contributing to better transportation systems.

Keywords: traffic congestion, machine learning, XGBoost, Random Forest, prediction, temporal features, weather data, event data, database, user interface.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.