Revealing Driver’s Natural Behavior—A GUHA Data Mining Approach

Esko Turunen and Klara Dolos
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Esko Turunen: Department of Mathematics and Statistics, Tampere University, Kalevantie 4, 33100 Tampere, Finland
Klara Dolos: Central Office for Information Technology in the Security Sector (ZITiS), Zamdorfer Street 88, 81677 München, Germany

Mathematics, 2021, vol. 9, issue 15, 1-10

Abstract: We investigate the applicability and usefulness of the GUHA data mining method and its computer implementation LISp-Miner for driver characterization based on digital vehicle data on gas pedal position, vehicle speed, and others. Three analytical questions are assessed: (1) Which measured features, also called attributes, distinguish each driver from all other drivers? (2) Comparing one driver separately in pairs with each of the other drivers, which are the most distinguishing attributes? (3) Comparing one driver separately in pairs with each of the other drivers, which attributes values show significant differences between drivers? The analyzed data consist of 94,380 measurements and contain clear and understandable patterns to be found by LISp-Miner. In conclusion, we find that the GUHA method is well suited for such tasks.

Keywords: natural driving behavior; data mining; GUHA method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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