  Increased interaction between and among pedestrians and vehicles in the
crowded urban environments of today gives rise to a negative side-effect: a
growth in traffic accidents, with pedestrians being the most vulnerable
elements. Recent work has shown that Convolutional Neural Networks are able to
accurately predict accident rates exploiting Street View imagery along urban
roads. The promising results point to the plausibility of aided design of safe
urban landscapes, for both pedestrians and vehicles. In this paper, by
considering historical accident data and Street View images, we detail how to
automatically predict the impact (increase or decrease) of urban interventions
on accident incidence. The results are positive, rendering an accuracies
ranging from 60 to 80%. We additionally provide an interpretability analysis to
unveil which specific categories of urban features impact accident rates
positively or negatively. Considering the transportation network substrates
(sidewalk and road networks) and their demand, we integrate these results to a
complex network framework, to estimate the effective impact of urban change on
the safety of pedestrians and vehicles. Results show that public authorities
may leverage on machine learning tools to prioritize targeted interventions,
since our analysis show that limited improvement is obtained with current
tools. Further, our findings have a wider application range such as the design
of safe urban routes for pedestrians or to the field of driver-assistance
technologies.
