Generalized Estimating Equation Model Based Recursive Partitioning: Application to Distracted Driving

Traditional statistical methods have used a coarse aggregation of data across subjects that may not be representative of any single individual. Even though Generalized Estimating Equations procedure extends generalized linear model to allow for analysis of repeated measurements or other correlated observations, the nonlinear relationship between independent variables and dependent variable could significantly hinder the model's performance. In this study, we propose Generalized Estimating Equation based tree model that combines the advantages of both models by separating the data set recursively into subsets with significantly different parameter estimates. For the best application of the proposed model, distracted driving on intersection is analyzed in this study. Previous studies have focused on evaluating the singular effect of individual geometry and human characteristic variables on driving behaviors. Interactions between variables associated with red-light running (e.g., cell phone usage, cell phone interface, and driver age groups) present different levels of distraction on red-light running. As an indicator of the sensitivity to distractions (referring to the distance being an impairment due to a secondary task), the drivers' distance to the intersection onset of yellow interval is partitioned into two groups (i.e., close and far distance) that maximally differentiate the distraction behavior. Proposed cell phone impact zones produce more significant impacts of distraction on red-light running, compared against dilemma zone. We identify interactions that are sensitive to red-light running and different as a function of the level of the speed and yellow interval duration. Drivers are more vulnerable to cell phone distractions when their location is near the stop line of the intersection at onset of yellow interval.