Machine learning applied to acoustic-based road traffic monitoring

• Published in: Procedia computer science 2022, Vol.207, p.1087-1095
• Main Authors: Marciniuk, Karolina, Kostek, Bożena
• Format: Article
• Language: English
• Subjects: machine learning; traffic road analysis; vehicle acoustics
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2022.09.164

The motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver's safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic detector are briefly described. Principles of several machine learning algorithms, data acquisition process, and information about the dataset built are explained. The study is conducted using the audio recordings prepared by the authors, registered in several locations and under different meteorological conditions of the road surface. For each recording containing a single-vehicle passage, a vector of 67 parameters extracted from the audio signal is calculated. Fisher Linear Discriminant Analysis and Regression Analysis, the fastest among algorithms employed, return the following values of accuracy: 0.968 and 0.978, precision: 0.919 and 0.853, recall: 0.882 and 0.974, and F1-score: 0.898 and 0.868 for vehicle type classification. In the case of the road pavement conditions, the obtained metrics are as follows: accuracy of 0.933, precision of 0.898, recall of 0.9, and F1-score of 0.884.