Generating a mobility-pattern database for urban traffic monitoring using distributed acoustic sensing

This work presents a methodology to generate a mobility-pattern database for urban traffic monitoring through distributed acoustic sensing. Registers from the continuous monitoring of a live experimental testbed provide three canonical types of events (buses, cars and pedestrians). Their separability for automatic detection and classification is inspected through PCA analysis of their approximate entropy and Hjorth parameters. An automatic detection and labeling procedure is presented and used to generate a database of labeled files accessible and usable for future machine learning approaches.