On Smart Traffic Management (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
Efficient traffic management systems are required due to the rising traffic congestion caused by the rapid rise of urbanization. The goal of this project, "On Smart Traffic Management," is to use cutting-edge machine learning algorithms to forecast traffic situations (Low, Normal, High, and Heavy). Although effective, the current system's use of Long Short-Term Memory (LSTM) and Logistic Regression may not adequately account for the intricacies of traffic dynamics. On the other hand, the suggested system combines the Random Forest, XGBoost, and Decision Tree algorithms to improve the accuracy and dependability of predictions. This research aims to create a reliable model for forecasting traffic patterns by utilizing historical traffic data, environmental variables, and real-time inputsWhile the back-end is created in Python to provide smooth data processing and analysis, the front-end is created with HTML, CSS, and JavaScript to create an intuitive user experience. An intelligent traffic management system that offers real-time traffic forecasts is the expected result, which will help reduce traffic and enhance urban mobility in general
Keywords: Bug Triaging; Machine Learning; Open-Source Software; Issue Classification; Software Maintenance