TY  - JOUR
PY  - 2017//
TI  - Modeling the impact of latent driving patterns on traffic safety using mobile sensor data
JO  - Accident analysis and prevention
A1  - Paleti, Rajesh
A1  - Sahin, Olcay
A1  - Cetin, Mecit
SP  - 92
EP  - 101
VL  - 107
IS  - 
N2  - 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.<br><br>Copyright © 2017 Elsevier Ltd. All rights reserved.<p />  <p>Language: en</p>
LA  - en
SN  - 0001-4575
UR  - http://dx.doi.org/10.1016/j.aap.2017.08.012
ID  - ref1
ER  -