Characterizing Driving Context from Driver Behavior
This work addresses the challenge of analyzing driving behavior in specific contexts for applications like traffic analysis, but it appears incremental as it builds on existing data-analytic methods.
The paper tackles the problem of characterizing driving context from spatiotemporal data to correlate driver behavior with traffic conditions, presenting DriveContext, a framework that identifies meaningful driving patterns and potential causes, with experimental results showing improvements over state-of-the-art methods.
Because of the increasing availability of spatiotemporal data, a variety of data-analytic applications have become possible. Characterizing driving context, where context may be thought of as a combination of location and time, is a new challenging application. An example of such a characterization is finding the correlation between driving behavior and traffic conditions. This contextual information enables analysts to validate observation-based hypotheses about the driving of an individual. In this paper, we present DriveContext, a novel framework to find the characteristics of a context, by extracting significant driving patterns (e.g., a slow-down), and then identifying the set of potential causes behind patterns (e.g., traffic congestion). Our experimental results confirm the feasibility of the framework in identifying meaningful driving patterns, with improvements in comparison with the state-of-the-art. We also demonstrate how the framework derives interesting characteristics for different contexts, through real-world examples.