Machine Learning Models for Real-Time Traffic Prediction: A Case Study in Urban Traffic Management

Abstract

This study introduces and evaluates the Long-term Traffic Prediction Network (LTPN), a specialized machine learning framework designed for real-time traffic prediction in urban environments. Utilizing a unique combination of convolutional and recurrent neural network layers, the LTPN model consistently outperforms established predictive models across various metrics. It demonstrates significantly lower error rates in both short and long-term traffic forecasts, highlighting its superior accuracy and reliability. The effectiveness of the LTPN model is underscored by its robust performance under diverse traffic conditions, making it a promising tool for enhancing the efficiency and responsiveness of intelligent transportation systems (ITS). This paper details the model's architecture, training processes, and a comprehensive comparison of its predictive capabilities against traditional models, providing clear evidence of its advantages in real-world applications.