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扮演着关键角色。特别是CIRCLES联盟项目需要依赖预测技术来减轻数据源延迟的影响。在MegaVanderTest实验取得成功后，本文旨在克服当前系统的局限性，开发更适配的方法以提升后续实验迭代中的实时交通状态估计性能。本文提出SA-LSTM：一种将空间维度上的自注意力机制与长短期记忆网络深度融合的深度预测方法，在实时中尺度交通预测中取得了最先进的成果。我们进一步将其扩展为n步SA-LSTM多步预测方法，该方法在实时运行条件下，其短期与长期预测的权衡表现优于传统多步预测方法。