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

Telecom SudParis, Institut Polytechnique de Paris, CNRS UMR5157, 91000 Evry, France
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Sensors 2020, 20(13), 3749; https://doi.org/10.3390/s20133749
Received: 27 May 2020 / Revised: 30 June 2020 / Accepted: 1 July 2020 / Published: 4 July 2020
(This article belongs to the Special Issue Smart Sensors and Devices in Artificial Intelligence)
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 management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting. View Full-Text
Keywords: air pollution; atmospheric data; deep learning; IoT; LSTM; machine learning; RNN; Sensors; smart cities; traffic flow; traffic forecasting air pollution; atmospheric data; deep learning; IoT; LSTM; machine learning; RNN; Sensors; smart cities; traffic flow; traffic forecasting
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Awan, F.M.; Minerva, R.; Crespi, N. Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks. Sensors 2020, 20, 3749.

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