Volume 39 Issue 2
Apr.  2021
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Article Contents
MA Xiaolei, YAO Liliang, SHEN Xuanliangan. Drivers' Travel Pattern Mining Based on OBD Data[J]. Journal of Transport Information and Safety, 2021, 39(2): 70-77. doi: 10.3963/j.jssn.1674-4861.2021.02.009
Citation: MA Xiaolei, YAO Liliang, SHEN Xuanliangan. Drivers' Travel Pattern Mining Based on OBD Data[J]. Journal of Transport Information and Safety, 2021, 39(2): 70-77. doi: 10.3963/j.jssn.1674-4861.2021.02.009

Drivers' Travel Pattern Mining Based on OBD Data

doi: 10.3963/j.jssn.1674-4861.2021.02.009
  • Received Date: 2020-12-19
  • The traditional travel pattern research mainly relies on questionnaires to analyze the driver's travel characteristics, the result of which is not objective. In order to solve the problem, the study analyzed and identifieddifferentdrivers' travel patterns based on the vehicle on-board diagnosticdata from 3 570 private cars in Beijing within two months. According to the parameters recorded from vehicles, a clustering algorithm called Clustering by Fast Search and Find of Density Peaks was used to classify different drivers into high-frequency travelers, commuting travelers, long-distance and occasional travelers and dangerous travelers, and analyzed from the aspects of average travel distance, travel frequency, travel time and dangerous driving behavior times of 100 km, to reflect the variability and regularity of driver's travel pattern. According to the clustering result, the multi-dimensional discrete Hidden Markov Model was used for modeling and measurement. Results indicate that the algorithm proposed shows good accuracy on the identification of drivers' travel patterns. For different kinds of drivers, the averagecorrect recognition rate exceed 91% while the highest recognition rete can reach 94.5%.

     

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