  Traffic congestion is a major urban issue due to its adverse effects on
health and the environment, so much so that reducing it has become a priority
for urban decision-makers. In this work, we investigate whether a high amount
of data on traffic flow throughout a city and the knowledge of the road city
network allows an Artificial Intelligence to predict the traffic flux far
enough in advance in order to enable emission reduction measures such as those
linked to the Low Emission Zone policies. To build a predictive model, we use
the city of Valencia traffic sensor system, one of the densest in the world,
with nearly 3500 sensors distributed throughout the city. In this work we train
and characterize an LSTM (Long Short-Term Memory) Neural Network to predict
temporal patterns of traffic in the city using historical data from the years
2016 and 2017. We show that the LSTM is capable of predicting future evolution
of the traffic flux in real-time, by extracting patterns out of the measured
data.
