Delineating race-specific driving patterns for identifying racial segregation

Transportation equity is a substantial concern for planners. Segregation and exposure analysis provide a lens from which community stakeholders can better decipher transportation equity challenges. This paper aims to expand racial segregation analysis beyond residential places to a more holistic activity space, including commuter populations. We filled existing research gaps on validity by calibrating Information Maximization (IM) model and a distance decay function to estimate race-specific driving patterns iteratively. A unique index of intergroup exposure and potential for contact between residents, workers, and commuters is proposed to understand the varying exposures different racial groups have with each other. We further identified the most racially-segregated road segments, residential and workplace areas, and how they become segregated based on the commuters' information. Given that exposure is a precursor to contact, understanding race-specific driving patterns is vital to understanding more extensive social mobility and segregation processes and their consequences for transport equity.