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Article Dans Une Revue IEEE Sensors Journal Année : 2021

Using noise pollution data for traffic prediction in smart cities: experiments based on LSTM recurrent neural networks

Résumé

Traffic prediction is one of the most important use cases for smart cities. Accurate traffic information is key to managing traffic issues. Many approaches that use traffic time series data to predict traffic flow have been proposed. In addition to traffic-specific parameters, some other features (called signatures) may be associated with road traffic, i.e., air and noise pollution. In this paper, we show how noise pollution and traffic time-series data were used to train Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNNs), which led to better traffic prediction on major roads in Madrid. This approach has already been used with pollution signatures. This work addresses a new potential investigation path closely related to the use of signature profiles and Artificial Intelligent techniques as a way to reduce the specialization of sensing infrastructure.
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Dates et versions

hal-03311498 , version 1 (31-07-2021)

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Faraz Malik Awan, Roberto Minerva, Noel Crespi. Using noise pollution data for traffic prediction in smart cities: experiments based on LSTM recurrent neural networks. IEEE Sensors Journal, 2021, 21 (18), pp.20722-20729. ⟨10.1109/JSEN.2021.3100324⟩. ⟨hal-03311498⟩
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