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Machine learning applied to acoustic-based road traffic monitoring

Abstrakt

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.

Cytowania

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Procedia Computer Science nr 207, strony 1087 - 1095,
ISSN: 1877-0509
Język:
angielski
Rok wydania:
2022
Opis bibliograficzny:
Marciniuk K., Kostek B.: Machine learning applied to acoustic-based road traffic monitoring// Procedia Computer Science -Vol. 207,iss. 207 (2022), s.1087-1095
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.procs.2022.09.164
Źródła finansowania:
  • COST_FREE
Weryfikacja:
Politechnika Gdańska

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