# A Capsule Network for Traffic Speed Prediction in Complex Road Networks

arXiv: https://arxiv.org/abs/1807.10603 · HTML: https://mlsanity.com/paper/1807.10603/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

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

- [Quick-CapsNet (QCN): A fast alternative to Capsule Networks](https://mlsanity.com/paper/2510.07600/) (2025)
- [Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic Prediction](https://mlsanity.com/paper/2103.02578/) (2021)

## Abstract (arXiv)

This paper proposes a deep learning approach for traffic flow prediction in complex road networks. Traffic flow data from induction loop sensors are essentially a time series, which is also spatially related to traffic in different road segments. The spatio-temporal traffic data can be converted into an image where the traffic data are expressed in a 3D space with respect to space and time axes. Although convolutional neural networks (CNNs) have been showing surprising performance in understanding images, they have a major drawback. In the max pooling operation, CNNs are losing important information by locally taking the highest activation values. The inter-relationship in traffic data measured by sparsely located sensors in different time intervals should not be neglected in order to obtain accurate predictions. Thus, we propose a neural network with capsules that replaces max pooling by dynamic routing. This is the first approach that employs the capsule network on a time series forecasting problem, to our best knowledge. Moreover, an experiment on real traffic speed data measured in the Santander city of Spain demonstrates the proposed method outperforms the state-of-the-art method based on a CNN by 13.1% in terms of root mean squared error.