Exploring the Use of Driver Attributes to Characterize Heterogeneity in Naturalistic Driving Behavior

Microsimulation models are powerful tools for exploring the impact of human driving behavior on the transportation system. One of the most heavily researched components of microsimulation models are car-following models, where human behavior is approximated using driver specific calibration coefficients. Inter-driver heterogeneity in calibration parameters has been observed in naturalistic data, but few studies have explored methods to characterize this heterogeneity. This research utilizes an 85-driver sample from the second Strategic Highway Research Program Naturalistic Driving Study dataset to calibrate over 100-hours of car-following data to three different car-following models: Gipps, Wiedemann 99, and Intelligent Driver Model. This enables a detailed exploration of the degree to which driver attributes-such as age, gender, and income-can be leveraged to account for inter-driver heterogeneity in car-following. The sample is segmented into smaller subsamples of data as a function of driver attributes. Statistically significant differences in parameter values are observed between the subsamples of data when age, miles driven last year, and income are used as characterizing traits. Moreover, it is shown that no single model outperforms the other models for all categories of data.