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

TL;DR

ST-Mamba is a deep learning model that improves traffic flow estimation accuracy and stability using limited data by capturing spatial-temporal patterns, offering a cost-effective urban traffic management solution.

Contribution

The paper introduces ST-Mamba, a novel deep learning framework combining CNN and Mamba techniques for enhanced traffic flow estimation with minimal data.

Findings

Achieves accurate traffic flow estimation with limited data

Demonstrates stability and robustness in real-world simulations

Offers a cost-effective alternative to extensive data collection

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.