Latest revision as of 19:22, 3 February 2021

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.

Comment: Accepted to be published at The 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2017)

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1710.05733

• http://arxiv.org/pdf/1710.05733

• http://dl.acm.org/ft_gateway.cfm?id=3139992&ftid=1934211&dwn=1,

http://dx.doi.org/10.1145/3139958.3139992 under the license http://www.acm.org/publications/policies/copyright_policy#Background

• https://dblp.uni-trier.de/db/journals/corr/corr1710.html#abs-1710-05733,

https://arxiv.org/pdf/1710.05733.pdf,

https://arxiv.org/pdf/1710.05733,

http://export.arxiv.org/pdf/1710.05733,

http://ui.adsabs.harvard.edu/abs/2017arXiv171005733M/abstract,

https://doi.org/10.1145/3139958.3139992,

https://dl.acm.org/citation.cfm?id=3139992,

https://academic.microsoft.com/#/detail/2767066510