A computational framework for measuring dynamic vehicular exposure and equity in the 15-min city

The 15-min city concept promises equitable access to essential services through walkable neighborhoods, yet traditional accessibility assessments fail to capture the dynamic impact of vehicular exposure on pedestrian experiences. This study introduces a novel computational framework that measures near-real-time, path-specific vehicular exposure during individual walking journeys by integrating large-scale mobility datasets including mobile phone trajectories (approximately 50% population coverage), GPS data from heavy trucks (75% coverage), and comprehensive public transit schedules in Beijing, China. We develop multi-scale exposure indices that distinguish between cars, trucks, and buses at road, location, and individual trip levels, revealing systematic inequities across socioeconomic groups: higher-income groups face intense car risks in amenity-rich urban areas, while lower-income groups are disproportionately burdened by truck traffic. Critically, we model an access gap that quantifies how traffic avoidance behavior constrains walkable access, revealing that vehicular exposure creates invisible barriers that systematically reduce the practical reach of the 15-min city. These findings compel a reinterpretation of walkability—from mere proximity to an experiential quality—and provide essential insights to identify and mitigate the environmental injustices that undermine equitable urban design.