# A Data-Driven Approach for Assessing Biking Safety in Cities

## TL;DR

This paper presents a data-driven model that assesses biking safety in cities using accident data and infrastructural features, enabling better policy decisions and cross-city safety comparisons.

## Contribution

It introduces a novel approach to model biking safety based on street-level data and evaluates its applicability across different urban environments.

## Key findings

- Model provides well-calibrated accident risk probabilities.
- Cross-city analysis reveals universal safety-related features.
- Model can simulate 'what-if' scenarios for policy planning.

## Abstract

With the focus that cities around the world have put on sustainable transportation during the past few years, biking has become one of the foci for local governments around the world. Cities all over the world invest in bike infrastructure, including bike lanes, bike parking racks, shared (dockless) bike systems etc. However, one of the critical factors in converting city-dwellers to (regular) bike users/commuters is safety. In this work, we utilize bike accident data from different cities to model the biking safety based on street-level (geographical and infrastructural) features. Our evaluations indicate that our model provides well-calibrated probabilities that accurately capture the risk of a biking accident. We further perform cross-city comparisons in order to explore whether there are universal features that relate to cycling safety. Finally, we discuss and showcase how our model can be utilized to explore "what-if" scenarios and facilitate policy decision making.