DAS Vehicle Signal Extraction Using Machine Learning in Urban Traffic Monitoring

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 efficient ML 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 (nontraffic) 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 isolation of car signals leads to a more accurate estimate of 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.