Network Level Spatial Temporal Traffic State Forecasting with Hierarchical-Attention-LSTM (HierAttnLSTM)
This work addresses traffic management challenges for urban planners and transportation agencies by improving spatial-temporal forecasting, though it is incremental as it builds on existing LSTM and attention methods.
The paper tackles network-level traffic state forecasting by integrating hierarchical LSTM networks with an attention mechanism, achieving higher prediction accuracy and effectively forecasting unusual congestion patterns compared to baseline models.
Traffic state data, such as speed, volume and travel time collected from ubiquitous traffic monitoring sensors require advanced network level analytics for forecasting and identifying significant traffic patterns. This paper leverages diverse traffic state datasets from the Caltrans Performance Measurement System (PeMS) hosted on the open benchmark and achieved promising performance compared to well recognized spatial-temporal models. Drawing inspiration from the success of hierarchical architectures in various Artificial Intelligence (AI) tasks, we integrate cell and hidden states from low-level to high-level Long Short-Term Memory (LSTM) networks with an attention pooling mechanism, similar to human perception systems. The developed hierarchical structure is designed to account for dependencies across different time scales, capturing the spatial-temporal correlations of network-level traffic states, enabling the prediction of traffic states for all corridors rather than a single link or route. The efficiency of designed attention-based LSTM is analyzed by ablation study. Comparative results with baseline LSTM models demonstrate that the Hierarchical Attention LSTM (HierAttnLSTM) model not only provides higher prediction accuracy but also effectively forecasts unusual congestion patterns. Data and code are made publicly available to support reproducible scientific research.