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Abstract
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.
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.procs.2022.09.164
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Procedia Computer Science
no. 207,
pages 1087 - 1095,
ISSN: 1877-0509 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Marciniuk K., Kostek B.: Machine learning applied to acoustic-based road traffic monitoring// Procedia Computer Science -Vol. 207, (2022), s.1087-1095
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.procs.2022.09.164
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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