Premium
Innovative modeling of naturalistic driving data: Inference and prediction
Author(s) -
Albert Paul S.
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7580
Subject(s) - statistical inference , computer science , statistical model , kinematics , hidden markov model , autoregressive model , inference , generalized linear mixed model , random effects model , crash , machine learning , econometrics , artificial intelligence , statistics , mathematics , medicine , physics , meta analysis , classical mechanics , programming language
Naturalistic driving studies provide opportunities for investigating the effects of key driving exposures on risky driving performance and accidents. New technology provides a realistic assessment of risky driving through the intensive monitoring of kinematic behavior while driving. These studies with their complex data structures provide opportunities for statisticians to develop needed modeling techniques for statistical inference. This article discusses new statistical modeling procedures that were developed to specifically answer important analytical questions for naturalistic driving studies. However, these methodologies also have important applications for the analysis of intensively collected longitudinal data, an increasingly common data structure with the advent of wearable devises. To examine the sources of variation between‐ and within‐participants in risky driving behavior, we explore the use of generalized linear mixed models with autoregressive random processes to analyzing long sequences of kinematic count data from a group of teenagers that have measurements at each trip over a 1.5‐year observation period starting after receiving their license. These models provide a regression framework for examining the effects of driving conditions and exposures on risky driving behavior. Alternatively, generalized estimating equations approaches are explored for the situation where we have intensively collected count measurements on a moderate number of participants. In addition to proposing statistical modeling for kinematic events, we explore models for relating kinematic events with crash risk. Specifically, we propose both latent variable and hidden Markov models for relating these 2 processes and for developing dynamic predictors of crash risk from longitudinal kinematic event data. These different statistical modeling techniques are all used to analyze data from the Naturalistic Teenage Driving Study, a unique investigation into how teenagers drive after licensure.