Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing and data mining of vehicular network data, such as driving speeds and GPS coordinates, present a promising and cost-effective alternative. Furthermore, minimizing data collection can significantly reduce overhead. However, limited data can lead to inaccuracies and instability in TFE. To address this, we introduce the spatial-temporal Mamba (ST-Mamba), a deep learning model combining a convolutional neural network (CNN) with a Mamba framework. ST-Mamba is designed to enhance TFE accuracy and stability by effectively capturing the spatial-temporal patterns within traffic flow. Our model aims to achieve results comparable to those from extensive data sets while only utilizing minimal data. Simulations using real-world datasets have validated our model's ability to deliver precise and stable TFE across an urban landscape based on limited data, establishing a cost-efficient solution for TFE.

翻译：交通流估计对于城市智能交通系统至关重要。传统的路边检测器受限于覆盖范围有限和成本高昂，而基于云计算和车载网络数据（如行驶速度和GPS坐标）的数据挖掘提供了一种前景广阔且经济高效的替代方案。此外，最大限度地减少数据收集可以显著降低开销。然而，有限的数据可能导致交通流估计的不准确和不稳定。为解决此问题，我们提出了时空Mamba，这是一种将卷积神经网络与Mamba框架相结合的深度学习模型。ST-Mamba旨在通过有效捕捉交通流中的时空模式，来提升交通流估计的准确性和稳定性。我们的模型目标是在仅使用极少数据的情况下，达到与使用大量数据集相当的结果。基于真实世界数据集的仿真验证了我们的模型能够在有限数据基础上，为城市区域提供精确且稳定的交通流估计，从而为交通流估计建立了一种经济高效的解决方案。