# Predicting the impact of urban change in pedestrian and road safety

arXiv: https://arxiv.org/abs/2202.01781 · HTML: https://mlsanity.com/paper/2202.01781/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 63 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://mlsanity.com/paper/1712.02616/) (2017)
- [Predicting the Driver's Focus of Attention: the DR(eye)VE Project](https://mlsanity.com/paper/1705.03854/) (2017)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [FARSA: Fully Automated Roadway Safety Assessment](https://mlsanity.com/paper/1901.06013/) (2019)
- [Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development](https://mlsanity.com/paper/1911.08007/) (2019)
- [Predicting Driver Self-Reported Stress by Analyzing the Road Scene](https://mlsanity.com/paper/2109.13225/) (2021)
- [Explainable, automated urban interventions to improve pedestrian and vehicle safety](https://mlsanity.com/paper/2110.11672/) (2021)
- [Limits of Predictability in Commuting Flows in the Absence of Data for Calibration](https://mlsanity.com/paper/1407.6256/) (2014)
- [Where are the Dangerous Intersections for Pedestrians and Cyclists: A Colocation-Based Approach](https://mlsanity.com/paper/2006.03131/) (2020)
- [Decongestion of urban areas with hotspot-pricing](https://mlsanity.com/paper/1604.07729/) (2016)
- [Urban Attractors: Discovering Patterns in Regions of Attraction in Cities](https://mlsanity.com/paper/1701.08696/) (2016)
- [Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness](https://mlsanity.com/paper/2006.03196/) (2020)

## Abstract (arXiv)

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.