  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.
