Predicting the impact of urban change in pedestrian and road safety

TL;DR

This paper presents a machine learning approach combining Street View imagery and historical accident data to predict how urban interventions affect pedestrian and road safety, with accuracy up to 80%.

Contribution

It introduces an integrated framework for predicting accident impact of urban changes using CNNs and network analysis, enhancing urban safety planning tools.

Findings

Predicts accident impact with 60-80% accuracy.

Identifies urban features influencing accident rates.

Provides a network-based assessment of urban safety interventions.

Abstract

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.