Hierarchical Flow Decomposition for Turning Movement Prediction at Signalized Intersections
This work addresses the challenge of adaptive signal control for traffic management, but it is incremental as it builds on existing deep learning methods with a hierarchical approach tailored to traffic data.
The study tackled the problem of predicting turning movements at signalized intersections by proposing HFD-TM, a hierarchical deep learning framework that first forecasts corridor flows and then expands to turning streams, achieving a mean absolute error of 2.49 vehicles per interval and reducing MAE by 5.7% compared to a Transformer and 27.0% compared to a GRU.
Accurate prediction of intersection turning movements is essential for adaptive signal control but remains difficult due to the high volatility of directional flows. This study proposes HFD-TM (Hierarchical Flow-Decomposition for Turning Movement Prediction), a hierarchical deep learning framework that predicts turning movements by first forecasting corridor through-movements and then expanding these predictions to individual turning streams. This design is motivated by empirical traffic structure, where corridor flows account for 65.1% of total volume, exhibit lower volatility than turning movements, and explain 35.5% of turning-movement variance. A physics-informed loss function enforces flow conservation to maintain structural consistency. Evaluated on six months of 15-minute interval LiDAR (Light Detection and Ranging) data from a six-intersection corridor in Nashville, Tennessee, HFD-TM achieves a mean absolute error of 2.49 vehicles per interval, reducing MAE by 5.7% compared to a Transformer and by 27.0% compared to a GRU (Gated Recurrent Unit). Ablation results show that hierarchical decomposition provides the largest performance gain, while training time is 12.8 times lower than DCRNN (Diffusion Convolutional Recurrent Neural Network), demonstrating suitability for real-time traffic applications.