A partial prior knowledge framework to discover the cycling visual environment coupling street view images and shared bike trajectory using deep learning

The effects of built environments on cycling have garnered considerable interests, and street view images (SVIs) as a proxy of built environments has been widely adopted to model cycling behaviours. However, most existing studies predominantly calculate indicators of built environment from segmented SVIs, and rely on prior knowledge, defined as pre-existing understandings of urban spaces, to verify well-known relationships or rules. This reliance limits the discovery of previously unrecognized environmental impacts on cycling. To address these challenges, we propose a partial prior knowledge framework for an explainable and accurate road-level cycling volume model that couples shared bike trajectory data and SVIs. This framework integrates deep learning and explainable machine learning to balance between prior-knowledge-based and end-to-end methods. Specifically, a Gradient-weighted Class Activation Mapping-enhanced deep learning method is applied to generate heatmaps of SVIs according to cycling volume data in an end-to-end mode, without any predefined assumptions about the street environment. Subsequently, semantic maps, as a typical form of prior knowledge, is incorporated to translate the highlighted regions into interpretable street visual elements with random forest regression and SHapley Additive exPlanations. The empirical study in Longgang District, Shenzhen demonstrates that our framework not only outperforms traditional approaches in prediction accuracy but also identifies several critical street visual elements and their impacts previously overlooked. The proposed framework improves human-artificial-intelligence interaction not only by enabling a deeper understanding of the complex impacts of street environments on cycling, but also by providing a generalization approach to extract human unknown knowledge from images and other non-conceptualized data.