ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data

• URL: http://arxiv.org/abs/2407.08558v1
• Date: Thu, 11 Jul 2024 14:43:03 GMT
• Title: ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data
• Authors: Doncheng Yuan, Jianzhe Xue, Jinshan Su, Wenchao Xu, Haibo Zhou,
• Abstract summary: Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. We introduce the spatial-temporal Mamba (ST-Mamba), a deep learning model combining a convolutional neural network (CNN) with a Mamba framework. Our model aims to achieve results comparable to those from extensive data sets while only utilizing minimal data.
• Score: 11.003036186451762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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