Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor

Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor

Monitoring the distribution of vehicles across the city is of great importance for urban traffic control. In particular, information on the number of vehicles entering and leaving a city, or moving between urban areas, gives a valuable estimate on potential bottlenecks and congestions. The possibili...

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Bibliographic Details
Main Authors:	Alessandro Crivellari, Euro Beinat
Format:	Article
Language:	English
Published:	MDPI AG 2020-12-01
Series:	Mathematics
Subjects:	deep learning LSTM neural networks traffic forecasting urban traffic distribution
Online Access:	https://www.mdpi.com/2227-7390/8/12/2233

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