Deep Learning for Traffic Data Mining

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 need to be improved in handling traffic’s complexity and dynamic nature in modern urban environments. The usage of advanced technologies in autonomous traffic volume is adapted to address the limitation. Traffic data are complex, and mathematical models relying upon simplified assumptions often must be revised to represent and analyze the complexity. With the revival of neural networks and large-scale datasets, deep learning-based solutions can be adapted to handle real-world traffic scenarios. Since neural networks can capture complex, non-linear relationships in traffic data, they can be utilized for intricate traffic patterns and dependencies that may be challenging for simple mathematical or regression models. We present a Deep Learning approach for accurately predicting traffic volume in Porto, Portugal. The proposed model leverages an extensive dataset encompassing historical traffic volume data from diverse sensors and monitoring systems. By harnessing the power of Recurrent Neural Networks (RNNs), the proposed model effectively captures temporal dependencies and generates precise forecasts for future traffic volume. The proposed approach surpasses previous methodologies in the same dataset, outperforming traditional time series models in different evaluation metrics. These findings demonstrate the practical potential of deep learning-based models in complex urban environments and offer a way to utilize sensor data. The proposed approach can adapt to changing traffic conditions and evolving patterns with infrequent training, making them suitable for dynamic environments.