Establishing a real-time traffic alarm in the city of Valencia with Deep
  Learning
Miguel Folgado Veronica Sanz Johannes Hirn Edgar Lorenzo-Saez Javier Urchueguia
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.