Characterizing Driving Context from Driver Behavior

TL;DR

This paper introduces DriveContext, a framework that analyzes driver behavior to characterize driving contexts by identifying patterns and potential causes, aiding in understanding how location and time influence driving.

Contribution

The paper presents a novel framework for characterizing driving contexts by extracting significant patterns and their causes from spatiotemporal data, outperforming existing methods.

Findings

Successfully identified meaningful driving patterns.

Improved accuracy over state-of-the-art methods.

Demonstrated real-world applicability with examples.

Abstract

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.