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Journal Article

Citation

Paleti R, Sahin O, Cetin M. Accid. Anal. Prev. 2017; 107: 92-101.

Affiliation

Transportation Research Institute, Old Dominion University, 135 Kaufman Hall, Norfolk, VA 23529, USA.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.aap.2017.08.012

PMID

28818683

Abstract

Smartphones are now equipped with sensors capable of recording vehicle performance data at a very fine temporal resolution in a cost-effective way. In this paper, mobile sensor data from smartphones was used to identify and quantify unsafe driving patterns and their relationship with traffic crash incidences. Statistical models that account for measurement error associated with microscopic traffic measures computed using mobile sensor data were developed. The models with microscopic traffic measures were shown to be statistically better than traditional models that only control for roadway geometry and traffic exposure variables. Also, generalized count models that account for measurement error, spatial dependency effects, and random parameter heterogeneity were found to perform better than standard count models.

Copyright © 2017 Elsevier Ltd. All rights reserved.


Language: en

Keywords

Crash frequency; Generalized ordered response; Latent driving patterns; Measurement error; Mobile sensors; Spatial dependency; Unobserved heterogeneity

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