  Accurate real-time traffic state forecasting plays a pivotal role in traffic
control research. In particular, the CIRCLES consortium project necessitates
predictive techniques to mitigate the impact of data source delays. After the
success of the MegaVanderTest experiment, this paper aims at overcoming the
current system limitations and develop a more suited approach to improve the
real-time traffic state estimation for the next iterations of the experiment.
In this paper, we introduce the SA-LSTM, a deep forecasting method integrating
Self-Attention (SA) on the spatial dimension with Long Short-Term Memory (LSTM)
yielding state-of-the-art results in real-time mesoscale traffic forecasting.
We extend this approach to multi-step forecasting with the n-step SA-LSTM,
which outperforms traditional multi-step forecasting methods in the trade-off
between short-term and long-term predictions, all while operating in real-time.
