# Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models

arXiv: https://arxiv.org/abs/1811.04745 · HTML: https://mlsanity.com/paper/1811.04745/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 11 of 11)

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- [Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data](https://mlsanity.com/paper/2202.05538/) (2022)
- [Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting](https://mlsanity.com/paper/2205.01480/) (2022)
- [Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting](https://mlsanity.com/paper/2206.11078/) (2022)
- [Spatial-temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction](https://mlsanity.com/paper/2105.10478/) (2021)
- [Predicting the traffic flux in the city of Valencia with Deep Learning](https://mlsanity.com/paper/2210.01630/) (2022)
- [Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations](https://mlsanity.com/paper/2210.07098/) (2022)
- [Establishing a real-time traffic alarm in the city of Valencia with Deep Learning](https://mlsanity.com/paper/2309.02010/) (2023)
- [1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting](https://mlsanity.com/paper/2310.02090/) (2023)

## Abstract (arXiv)

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.