Linking Driver Socioeconomic Profiles to Speeding and Hard-Braking Behaviors: Insights from High-Resolution Vehicle Trajectory Data and Departure-Based Locations

Abstract

High-resolution trajectories generated by vehicle telematics can provide detailed, neutral, and naturalistic insights into driving behaviors across large-scale transportation systems. This research explores speeding and hard-braking behaviors extracted from the high-resolution vehicle trajectory (HRVT) data across various socioeconomic profiles, potentially complementing conventional traffic crash records and survey data. Through vehicle movement trajectory analytics, spatial characteristics of these driving events are linked to socioeconomic profiles based on HRVT departure locations. Results reveal positive correlations between speeding rates and household median income, educational attainment, drivers aged 20–59, and “Black or African American” populations, while negative correlations exist with disability rates and older drivers. Similarly, hard-braking rates positively correlate with drivers aged 20–59 and “Black or African American” populations, and negatively with disability rates and older drivers. Notably, “Non-Hispanic White” populations showed positive correlations in contrast to negative correlations with “Hispanic White” populations. Methodologically, multiscale geographically weighted regression more effectively addresses data autocorrelation in geospatial patterns compared to ordinary least squares regression. This study introduces an innovative application of emerging data sources to understand driving behavior across socioeconomic profiles, supporting transportation agencies in developing targeted safety interventions.