ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data
<|reference_start|>ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data: 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.<|reference_end|>
@article{yuan2024st-mamba:,
    title={ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery
  using Limited Data},
    author={Doncheng Yuan, Jianzhe Xue, Jinshan Su, Wenchao Xu, and Haibo Zhou},
    journal={arXiv preprint arXiv:2407.08558},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08558},
    primaryClass={cs.AI}
}