Traffic Prediction System using Data Science

Authors:

Sable Kalyani Bhairvnath, Gadhave Kalyani Mahesh, Prof. Patil R. Sir

ABSTRACT

The Traffic Prediction System using Data Science in Java is designed to analyze and predict traffic congestion levels based on historical and real-time data. With the rapid increase in vehicles and urbanization, traffic management has become a significant challenge in modern cities. This project aims to address this issue by developing a predictive model that can classify traffic conditions into categories such as Low, Medium, and High.

The system utilizes machine learning techniques implemented in Java using the Weka library. It considers various input parameters such as time, day, vehicle count, and weather conditions to train the prediction model. A Decision Tree algorithm (J48) is used to build the model due to its simplicity, interpretability, and effectiveness in classification tasks.