# Network Level Spatial Temporal Traffic State Forecasting with Hierarchical-Attention-LSTM (HierAttnLSTM)

arXiv: https://arxiv.org/abs/2201.05760 · HTML: https://mlsanity.com/paper/2201.05760/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 15 Jan 2022.

## Builds on (most cited first, 18 of 18)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Hierarchical Multiscale Recurrent Neural Networks](https://mlsanity.com/paper/1609.01704/) (2016)
- [STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting](https://mlsanity.com/paper/1905.10069/) (2019)
- [Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values](https://mlsanity.com/paper/2005.11627/) (2020)
- [Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning](https://mlsanity.com/paper/1511.03476/) (2015)
- [Learning dynamic and hierarchical traffic spatiotemporal features with Transformer](https://mlsanity.com/paper/2104.05163/) (2021)
- [Grid Long Short-Term Memory](https://mlsanity.com/paper/1507.01526/) (2015)
- [Travel time prediction for congested freeways with a dynamic linear model](https://mlsanity.com/paper/2009.01016/) (2020)
- [Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days](https://mlsanity.com/paper/2011.05073/) (2020)

## Built on by (most cited first, 2 of 2)

- [From Optimization to Prediction: Transformer-Based Path-Flow Estimation to the Traffic Assignment Problem](https://mlsanity.com/paper/2510.19889/) (2025)
- [Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting](https://mlsanity.com/paper/2508.00884/) (2025)

## Abstract (arXiv)

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.