  In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to
forecast a regression model for time dependent data. These algorithm's are
designed to handle Floating Car Data (FCD) historic speeds to predict road
traffic data. For this we aggregate the speeds into the network inputs in an
innovative way. We compare the RMSE thus obtained with the results of a simpler
physical model, and show that the latter achieves better RMSE accuracy. We also
propose a new indicator, which evaluates the algorithms improvement when
compared to a benchmark prediction. We conclude by questioning the interest of
using deep learning methods for this specific regression task.
