• Version
• Download 8
• File Size 358.33 KB
• File Count 1
• Create Date 21 March 2026
• Last Updated 21 March 2026

Real-Time Traffic Prediction using Deep Spatiotemporal Learning

Payal Wani..( Department of Data Science
Dr. D. Y. Patil Arts, Commerce and Science College Pimpri)
Jaywardhan Yadav..(Department of Data Science
Dr. D. Y. Patil Arts, Commerce and Science College Pimpri)

Abstract - This study presents a machine learning-based approach for real-time traffic prediction. Traditional traffic prediction models fail to capture complex relationships between traffic conditions and environmental factors. In this research, a neural network model is used to predict traffic speed based on features such as traffic volume, temperature, and weather conditions. The dataset was preprocessed and normalized to improve model performance. The model was trained and evaluated using standard regression metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² score. The proposed model achieved an accuracy of over 90%, demonstrating strong predictive capability. Visualization techniques such as loss curves, residual plots, and prediction graphs were used to analyze model performance. The system can be applied in smart city traffic management and route optimization..
Key Words: Traffic prediction, machine learning, neural network, regression, smart city, data science