TripMD: Driving patterns investigation via motif analysis

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Processing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy making. A common strategy to analyze driving behavior is to study the maneuvers being performance by the driver. In this paper, we propose TripMD, a system that extracts the most relevant driving patterns from sensor recordings (such as acceleration) and provides a visualization that allows for an easy investigation. Additionally, we test our system using the UAH-DriveSet dataset, a publicly available naturalistic driving dataset. We show that (1) our system can extract a rich number of driving patterns from a single driver that are meaningful to understand driving behaviors and (2) our system can be used to identify the driving behavior of an unknown driver from a set of drivers whose behavior we know.

Original languageEnglish
Article number115527
Pages (from-to)1-12
Number of pages12
JournalExpert Systems with Applications
Publication statusPublished - 1 Dec 2021


  • Acceleration
  • Driving behaviors
  • Motif discovery
  • Road safety
  • Sensors
  • Time-series


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