Print Email Facebook Twitter Time-series clustering for pattern recognition of speed and heart rate while driving Title Time-series clustering for pattern recognition of speed and heart rate while driving: A magnifying lens on the seconds around harsh events Author Tselentis, D. (TU Delft Policy & Strategy; TU Delft Safety and Security Science) Papadimitriou, E. (TU Delft Safety and Security Science; TU Delft Policy & Strategy) Date 2023 Abstract Driving pattern recognition has been applied for the purposes of driving styles identification and harsh driving events detection. However, the evolution of driving behavior around and especially before such events has not been investigated at a microscopic level. The objective of this research is to reveal existing driving patterns around harsh events at the driving ‘pulse’ level i.e. a few seconds before and after the event. For that purpose, a time-series clustering approach is applied on speed and heart rate metrics of individual drivers using data collected from a large naturalistic driving study. Results show that there are distinct speed patterns before harsh braking, harsh acceleration, and harsh cornering events. A deceleration is identified shortly before most harsh acceleration and cornering events, which possibly indicates reckless behavior, i.e. drivers not dedicating enough time to smoothly brake before cornering, or of a brief ‘decision-making’ moment before the harsh manoeuvre. On the contrary, speed seems to be steady before harsh braking events. Regarding heart rate, the analysis revealed certain patterns only after raw data were cleansed and filtered. These patterns may show increasing, decreasing or variable heart rate trends, which may correspond to different stress patterns of drivers around harsh events. Finally, we introduce the concept of driving pattern consistency, which can reveal the share of individual drivers that follow the same harsh event pattern. It is indicated that more than half of the drivers are not consistent, suggesting that driving patterns around harsh events may be more context-related than driver personality-related. Subject Driving Pattern RecognitionDriving pulseHeart-Rate patternsSpeed PatternsTime-series ClusteringUnsupervised Learning To reference this document use: http://resolver.tudelft.nl/uuid:5e0291fc-7d5f-4630-a386-addd064b4ee9 DOI https://doi.org/10.1016/j.trf.2023.09.010 ISSN 1369-8478 Source Transportation Research. Part F: Traffic Psychology and Behaviour, 98, 254-268 Part of collection Institutional Repository Document type journal article Rights © 2023 D. Tselentis, E. Papadimitriou Files PDF 1_s2.0_S1369847823001948_main.pdf 5.28 MB Close viewer /islandora/object/uuid:5e0291fc-7d5f-4630-a386-addd064b4ee9/datastream/OBJ/view