A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction

Abstract

Spatio-temporal forecasting is an open research field whose interest is growing exponentially. In this work we focus on creating a complex deep neural framework for spatio-temporal traffic forecasting with comparatively very good performance and that shows to be adaptable over several spatio-temporal conditions while remaining easy to understand and interpret. Our proposal is based on an interpretable attention-based neural network in which several modules are combined in order to capture key spatio-temporal time series components. Through extensive experimentation, we show how the results of our approach are stable and better than those of other state-of-the-art alternatives.

arXiv comment16 pages, 14 figures

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Cited by in corpus (1)

• Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies