Establishing a real-time traffic alarm in the city of Valencia with Deep Learning
This work addresses traffic management and pollution reduction for city decision-makers in Valencia, but it is incremental as it applies an existing LSTM method to a new dataset.
The study tackled urban traffic pollution by analyzing traffic flux and pollutant correlations in Valencia, Spain, and developed a real-time alarm system using LSTM neural networks to predict unusually high traffic in streets within 30 minutes, achieving predictions based on 2018 training and 2019 testing data.
Urban traffic emissions represent a significant concern due to their detrimental impacts on both public health and the environment. Consequently, decision-makers have flagged their reduction as a crucial goal. In this study, we first analyze the correlation between traffic flux and pollution in the city of Valencia, Spain. Our results demonstrate that traffic has a significant impact on the levels of certain pollutants (especially $\text{NO}_\text{x}$). Secondly, we develop an alarm system to predict if a street is likely to experience unusually high traffic in the next 30 minutes, using an independent three-tier level for each street. To make the predictions, we use traffic data updated every 10 minutes and Long Short-Term Memory (LSTM) neural networks. We trained the LSTM using traffic data from 2018, and tested it using traffic data from 2019.