A framework for evaluating aggressive driving behaviors based on in-vehicle driving records

•This study developed a framework to cluster drivers’ behavior using driving records.•The framework was applied to large-scale data from real driving environment.•Representative driving patterns were identified on the cluster map.•The cluster map can be used as a reference in evaluating other driver...

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Bibliographic Details
Published in:Transportation research. Part F, Traffic psychology and behaviour Traffic psychology and behaviour, 2019-08, Vol.65, p.610-619
Main Authors: Lee, Jooyoung, Jang, Kitae
Format: Article
Language:English
Subjects:
Aggressive driving behavior
Automobile drivers
Automobile driving
Change detection
Clustering
Coders
Feature extraction
In vehicle
In-vehicle driving record
Large-scale data
Occupational safety
Taxicabs
Traffic accidents & safety
Traffic safety
Two-level clustering
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Summary:•This study developed a framework to cluster drivers’ behavior using driving records.•The framework was applied to large-scale data from real driving environment.•Representative driving patterns were identified on the cluster map.•The cluster map can be used as a reference in evaluating other driver’s behavior. Driving behavior is how drivers respond to actual driving environments and a major factor for road traffic safety. Recent advances in in-vehicle sensors facilitate continuous monitoring of driving behaviors; large-scale driving data have been accumulated. This study develops a framework to evaluate large-scale driving records and to establish clusters that can be used to identify potentially aggressive driving behaviors. The framework employs three steps of data analytic methods: abrupt change detection to extract meaningful driving events from raw data, feature extraction using an auto-encoder, and two-level clustering. This framework is applied to real driving data that were obtained from 43 taxis in Korean metropolitan cities. The application shows that the framework can characterize driving patterns from large-scale driving records and identify clusters with high potential for aggressive driving. The findings imply that the outcome clusters represent the norm of driving behavior and thus can be used as a reference in diagnosing other drivers’ behavior.
ISSN:1369-8478
1873-5517
DOI:10.1016/j.trf.2017.11.021