Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons

TL;DR

This paper introduces a machine learning approach using pairwise image comparisons and a siamese neural network to predict human perceptions of cycling safety in various environments, enabling continuous and scalable safety assessments.

Contribution

It presents a novel method combining pairwise image comparisons with deep learning to model human safety perceptions for cycling environments, including ties.

Findings

The model accurately predicts perceived safety from images.

The approach enables scalable, real-time safety perception assessments.

It can be deployed across different locations with available street-view images.

Abstract

Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. If individuals perceive an environment as unsafe for cycling, it is likely that they will prefer other means of transportation. Yet, capturing and understanding how individuals perceive cycling risk is complex and often slow, with researchers defaulting to traditional surveys and in-loco interviews. In this study, we tackle this problem. We base our approach on using pairwise comparisons of real-world images, repeatedly presenting respondents with pairs of road environments and asking them to select the one they perceive as safer for cycling, if any. Using the collected data, we train a siamese-convolutional neural network using a multi-loss framework that learns from individuals' responses, learns preferences directly from images, and includes ties (often discarded in the literature). Effectively, this model learns to predict human-style perceptions, evaluating which cycling environments are perceived as safer. Our model achieves good results, showcasing this approach has a real-life impact, such as improving interventions' effectiveness. Furthermore, it facilitates the continuous assessment of changing cycling environments, permitting short-term evaluations of measures to enhance perceived cycling safety. Finally, our method can be efficiently deployed in different locations with a growing number of openly available street-view images.