Modeling the impact of street-level built environment on cyclists’ route choice using street view images and GPS data

Abstract

Understanding how cyclists select their routes is crucial for designing infrastructure that promotes sustainable urban mobility. This study investigates the impact of street-level built environment features on cyclists’ route choices by integrating GPS trajectory data with Google Street View imagery. Using data collected in Utrecht, the Netherlands, we develop a comprehensive cycling network and employ deep learning-based image segmentation to quantify built environment attributes, such as greenery, road, and buildings. A path size logit model is applied to assess the influence of these factors on route selection. Our findings reveal that cyclists prefer routes with lower traffic light density, more greenery, and fewer motorized vehicles, while they tend to avoid routes with wide roads and dense building facades. Interestingly, while an overall green environment encourages cycling, an uneven distribution of greenery can deter cyclists. These insights highlight the need for a well-balanced urban streetscape to enhance cycling appeal. The results provide recommendations for urban planners to improve cycling infrastructure, emphasizing the importance of integrating green and blue spaces while ensuring safe and efficient travel paths. Future research will explore the impact of additional factors, such as noise and air quality, and further refine our understanding of cyclist route preferences.

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