The neighbourhood built environment affects driving behaviours of older adults: a combined geographic information systems and machine learning method

Driving space is considered as the transaction between built environment features and driving behaviour. Driving keeps people active and engaged, particularly in later life. Using Geospatial Information Systems (GIS) and machine learning, this study examined the driving space of older drivers (aged ≥65; n = 134) living in St. Louis City, St. Louis County, USA from 1 January 2019, to 31 December 2019. Driving variables, such as total distance, trip frequency, ratio of short trips long trips, were analyzed. Built environment measures included transit accessibility, land use mix, and road network characteristics. Our findings indicate that the most important features predictive of driving space of older adults were public transit density and land use diversity within residential areas. This study demonstrates the non-linear relationship between built environment factors and driving space variables. Total distance has a complex relationship with each built environment variable. The differences in short-distance and long-distance driving are linked to varied land use types, balanced transport density, and intersection density. These findings highlight the value of using in-vehicle monitoring technologies to determine how specific characteristics of the built environment can influence everyday driving behaviours in later life.