Understanding the impact of street environments on traffic crash risk from the perspective of aging people : an interpretable machine learning approach

Abstract

As the aging population grows rapidly, the traffic risks faced by older adults have become a growing concern for age-friendly transportation planning. While prior studies have investigated the relationship between traffic crashes and the built environment, they often treat the population as homogeneous and ignore the fine-grained characteristics of the street environment. This study addresses these gaps by examining how fine-grained street environments influence crash risks, with a particular focus on aging people. Specifically, we use segmented street view images to train models that predict crash risk levels based on normalized crash frequencies, with separate models developed for older and non-older populations. Interpretable machine learning methods are then employed to identify key environmental contributors and to compare their spatial contribution patterns across age groups. Our findings reveal that the traffic crash risk of older adults is more strongly influenced by street environment indicators, both positive and negative, indicating their greater sensitivity to environmental conditions. Moreover, the contribution of street features differs significantly between age groups, not only in overall trends but also in the spatial patterns of their impact. Our research uncovers age-specific interactions with the street environment and emphasizes the need for differentiated transportation planning approaches.