Urban visual clusters and road transport fatalities: A global city-level image analysis

Abstract

Road traffic crashes are among the leading causes of death and injury worldwide. While urban planning and design are known to influence road safety, it is not clear how features of the built environment contribute to traffic fatalities. In this study, we analyze road fatality data from 106 cities across six continents via a combination of computer vision and unsupervised clustering on 26.8 million Google Street View images. We use deep learning tools to extract 25 features from the images. Among these features, 19 are relatively static built environment features, and 6 are dynamic usage-related features (such as pedestrians, cars, buses, and bikes). On the basis of the built environment features, we group the urban streetscapes into six distinct visual clusters. We then examine how these clusters are related to city-level road fatality rates when various control variables (e.g., population size, carbon emissions, income, road length, road safety policy, and continent) and dynamic features are combined. Our findings show that cities with Open Arterials streetscape (extensive road surface, open-sky views, and railings) tend to have higher road fatality rates. After accounting for differences in the built environment, cities with better public transit (proxied by buses detected) tend to have fewer traffic deaths—specifically, a 1% increase in bus presence is linked to a 0.35% decrease in fatalities per 100,000 people. This study demonstrates the power of using widely available street view imagery to uncover global disparities in urban design and their connection to road safety.