PEMS

This study focuses on the application of the PeMS dataset, specifically the Pems03, Pems04, and Pems08 subsets, for traffic flow prediction and analysis. These datasets, sourced from the California Performance Measurement System (PeMS), provide real-time traffic sensor data from different regions, including the Bay Area (Pems03), Southern California (Pems04), and the Los Angeles area (Pems08). We leverage advanced machine learning and deep learning techniques, such as Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs), to model both spatial and temporal dependencies in traffic data. The primary aim of this research is to improve the accuracy of traffic flow forecasting by incorporating the distinctive features of each dataset, such as regional traffic patterns, sensor placement, and varying traffic conditions. Experimental results show significant improvements in prediction performance for short-term and long-term traffic flow forecasts, compared to traditional methods. Our findings have important implications for traffic management systems, providing a foundation for more effective congestion management and optimized routing strategies in urban traffic networks.