DAS Vehicle Signal Extraction Using Machine Learning in Urban Traffic Monitoring

Abstract

Distributed acoustic sensing (DAS) is a new technology for recording vibration signals using optical fibers, and is advantageous over traditional seismic geophones given high spatial sampling density, real-time monitoring capabilities and relatively low cost for large-scale data acquisition. In recent years, progress in applications of the DAS technique has been achieved in near-surface imaging, earthquake detection, and urban traffic monitoring. In this study, we propose to apply a machine learning (ML) method to recognize and extract vehicle signals from DAS data acquired in a typical urban environment in Hangzhou, China. To design an efficientML framework, we apply a series of processing steps to eliminate noise and strengthen the vehicle signal, which is crucial for preparing high-quality labels. Initially, a total of 190 features (62 1-D features and 128 2-D features) are extracted from raw data, which are filtered down to 31 through univariate feature selection, random forest, and similarity analyses. These selected features are classified into (traffic) signal or (non-traffic) noise using the classic ML method of support vector machine (SVM). The resulting model enables robustly extracting vehicle signals with only a small (e.g., 10) training dataset and achieve an overall accuracy of about 80% on the test data. We further demonstrate the application of city traffic monitoring by considering the slope and coherence of the extracted vehicle signals. The preservation of car signals leads to a more accurate estimate in vehicle speed and volume. This study highlights the potential of real-time monitoring of speed and volume of traffic flow using existing city infrastructure and sheds light on the promising applications of the DAS technique in developing smart cities.

Topics & Concepts