Deep Learning for Traffic Data Mining

Abstract

Traditionally, traffic management relies on several methods, including but not limited to signage, traffic signaling, and manual control. Traffic engineers analyze traffic patterns collected through various sensors and use their expertise to manage traffic flow. While traditional traffic management methods have been adequate to some extent, they often struggle to handle the complexity and dynamic nature of traffic in modern urban environments. To address these limitations, advanced technologies in autonomous traffic volume prediction have been developed. Traffic data are inherently complex, and traditional mathematical models, which rely on simplified assumptions, frequently fail to accurately represent and analyze this complexity. With the rise of neural networks and the availability of large-scale datasets, deep learning-based solutions have emerged as a robust approach to managing real-world traffic scenarios. Neural networks’ ability to cap￾ture complex, non-linear relationships in traffic data makes them well-suited for handling intricate traffic patterns that are challenging for simpler models. In this study, we present a deep learning approach for accurately predicting traffic volume in Porto, Portugal. Our model leverages an extensive dataset encompassing historical traffic volume data from diverse sensors and monitoring systems. The experiment was conducted in two phases: Phase 1, using a 4-month dataset, and Phase 2, using a 15-month dataset. In Phase 1, Re￾current Neural Networks (RNNs) demonstrated superior performance with an R-squared value of 0.9953 and a MAPE of 4.6%, effectively capturing temporal dependencies to gen￾erate precise traffic volume forecasts. In Phase 2, the Gated Recurrent Unit (GRU) model outperformed other models, achieving an R-squared value of 0.9919 and a MAPE of 5.3%, showcasing its ability to handle longer time series data with complex patterns and depen￾dencies. By harnessing the power of these deep learning models, our approach surpasses previous methodologies on the same dataset, outperforming traditional time-series models across different evaluation metrics such as MAE and RMSE. These findings demonstrate the practical potential of deep learning-based models in complex urban environments and offer a robust way to utilize sensor data. The proposed models can adapt to changing traffic conditions and evolving patterns with infrequent training, making them suitable for dynamic environments.