Road traffic attributes prediction using deep learning hybridization by the traffic fundamental diagram

The prediction of road traffic attributes, such as flow, speed, and density, plays a crucial role in traffic management systems. Deep learning (DL) techniques provide an effective way to predict the future based on historical data. However, it is often impractical to measure all road traffic attributes for DL-based predictions. Given that traffic flow is the most frequently measured traffic attribute, there is a large body of research on DL-based traffic flow prediction. Nevertheless, using only traffic flow data is insufficient to comprehensively depict road traffic conditions. Traffic fundamental diagrams (TFDs) offer a way to estimate other traffic attributes, based on traffic flow. However, the inherent non-monotonic relationship between flow and density poses a significant challenge because a specific flow may correspond to two different densities, reflecting uncongested and congested road conditions. To address this issue, we propose a novel framework for predicting road traffic attributes by hybridizing DL with TFDs. The proposed framework comprises two streams. In the supplementary stream, traffic data (flow, density, and speed) are used to calibrate TFDs and generate congestion labels, which are then used to train a congestion predictor. The main stream relies solely on traffic flow data, aligning with real-world scenarios. One DL model predicts future flow, while another predicts congestion labels based on historical flow data. These labels, combined with the predicted flow, enable the calibrated TFDs to determine density and speed values. Experiments based on a case study of a freeway in Melbourne, Australia, demonstrate the effectiveness of the proposed framework.