Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons
Journal:
arXiv
Published Date:
Dec 13, 2024
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.
