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A Capsule Network for Traffic Speed Prediction in Complex Road Networks by Kim, Youngjoo; Wang, Peng; Zhu, Yifei; Mihaylova, Lyudmila is a scholarly article available to read on EtoBox.

What is A Capsule Network for Traffic Speed Prediction in Complex Road Networks about?

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 met

Author

Kim, Youngjoo; Wang, Peng; Zhu, Yifei; Mihaylova, Lyudmila

Published

2018

Language

EN