Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic manage...

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Bibliographic Details
Main Authors: Faraz Malik Awan, Roberto Minerva, Noel Crespi
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Sensors
Subjects:
air pollution
atmospheric data
deep learning
IoT
LSTM
machine learning
Online Access:https://www.mdpi.com/1424-8220/20/13/3749

Internet

https://www.mdpi.com/1424-8220/20/13/3749

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