Semiparametric Bayesian models for evaluating time‐variant driving risk factors using naturalistic driving data and case‐crossover approach

Driver behavior is a major contributing factor for traffic crashes, a leading cause of death and injury in the United States. The naturalistic driving study (NDS) revolutionizes driver behavior research by using sophisticated nonintrusive in‐vehicle instrumentation to continuously record driving data. This paper uses a case‐crossover approach to evaluate driver‐behavior risk. To properly model the unbalanced and clustered binary outcomes, we propose a semiparametric hierarchical mixed‐effect model to accommodate both among‐strata and within‐stratum variations. This approach overcomes several major limitations of the standard models, eg, constant stratum effect assumption for conditional logistic model. We develop 2 methods to calculate the marginal conditional probability. We show the consistency of parameter estimation and asymptotic equivalence of alternative estimation methods. A simulation study indicates that the proposed model is more efficient and robust than alternatives. We applied the model to the 100‐Car NDS data, a large‐scale NDS with 102 participants and 12‐month data collection. The results indicate that cell phone dialing increased the crash/near‐crash risk by 2.37 times (odds ratio: 2.37, 95% CI, 1.30‐4.30) and drowsiness increased the risk 33.56 times (odds ratio: 33.56, 95% CI, 21.82‐52.19). This paper provides new insight into driver behavior risk and novel analysis strategies for NDS studies.