Understanding cycling route choice behaviour through street-level images and discrete choice models

This study examines cyclists’ preferences regarding infrastructural and environmental attributes, including lane type, pavement, lane colour, and the presence of greenery, water, and buildings. To do so, we conduct an image-based stated route-choice experiment in which respondents evaluate alternatives that vary in cycling environment, travel time, and the number of traffic lights. For the survey design, we used over six thousand real-world street-level images of the cycling environment, providing a more realistic choice context for respondents than could be achieved with a traditional stated-choice experiment based on tabular data with text and numbers. We analyse these route-choice data using two complementary modelling approaches: traditional discrete choice models with interpretable environmental attributes derived from GIS data, and a recently proposed computer vision-enriched discrete choice model that integrates image-based information directly into the discrete choice modelling framework. Results show that cycling environments are important factors in explaining cyclist route choice behaviour, with cyclists preferring green areas and separated cycling lanes over urban areas and mixed-traffic roads. In addition, we use the developed models to map where the cycling network in Rotterdam, our case study, aligns —or conflicts— with cyclists’ preferences. Finally, we demonstrate how the developed model can provide planners with ex-ante evidence on the prospective effectiveness of proposed infrastructure upgrades.