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.

翻译：准确的实时交通状态预测在交通控制研究中起着关键作用。特别是，CIRCLES联盟项目亟需预测技术来缓解数据源延迟的影响。在MegaVanderTest实验取得成功后，本文旨在克服当前系统的局限性，开发一种更适合的方法来改进下一轮实验的实时交通状态估计。本文提出了SA-LSTM，这是一种将空间维度上的自注意力机制与长短期记忆网络相结合的深度预测方法，在实时中观交通流预测中达到了最先进水平。我们将此方法扩展至多步预测，提出了n步SA-LSTM，该方法在短期与长期预测的权衡中优于传统的多步预测方法，且始终保持实时运行。