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.

本文介绍了 SA-LSTM，一种深度预测方法，通过在空间维度上集成自注意力 (SA) 和长短期记忆 (LSTM)，在实时中尺度交通预测方面取得了最先进的结果，并将此方法扩展到多步预测，从而在短期和长期预测之间取得了传统多步预测方法无法匹敌的平衡，并且在实时运行中实现了这些功能。