Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks

Daniel Vélez-Serrano, Alejandro Álvaro-Meca, Fernando Sebastián-Huerta and Jose Vélez-Serrano
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Daniel Vélez-Serrano: Department of Statistics and Operations Research, Complutense University, 28040 Madrid, Spain
Alejandro Álvaro-Meca: Department of Preventive Medicine and Public Health, Rey Juan Carlos University, Avd, Atenas SN, 28922 Madrid, Spain
Fernando Sebastián-Huerta: Innova-TSN, 28020 Madrid, Spain
Jose Vélez-Serrano: Department of Computer Science, Rey Juan Carlos University, 28922 Madrid, Spain

Mathematics, 2021, vol. 9, issue 9, 1-16

Abstract: Due to the need to predict traffic congestion during the morning or evening rush hours in large cities, a model that is capable of predicting traffic flow in the short term is needed. This model would enable transport authorities to better manage the situation during peak hours and would allow users to choose the best routes for reaching their destinations. The aim of this study was to perform a short-term prediction of traffic flow in Madrid, using different types of neural network architectures with a focus on convolutional residual neural networks, and it compared them with a classical time series analysis. The proposed convolutional residual neural network is superior in all of the metrics studied, and the predictions are adapted to various situations, such as holidays or possible sensor failures.

Keywords: convolutional neural network; residual neural network; ARIMA; spatio-temporal; traffic flow (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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