Discovering Cyclists' Street Visual Preferences Through Multi-Source Big Data Using Deep Inverse Reinforcement Learning

Cycling has gained global popularity for its health benefits and positive urban impacts. To effectively promote cycling, early studies have extensively investigated the relationship between cycling behaviors and environmental factors, especially cyclists' preferences when making route decisions. However, these studies often struggle to comprehensively describe detailed cycling procedures at a large scale due to data limitations, and they tend to overlook the complex nature of cyclists' preferences. To address these issues, we propose a novel framework aimed to quantify and interpret cyclists' complicated street visual preferences from cycling records by leveraging maximum entropy deep inverse reinforcement learning (MEDIRL) and explainable artificial intelligence (XAI). Implemented in Bantian Sub-district, Shenzhen, we adapt MEDIRL model for efficient estimation of cycling reward function by integrating dockless-bike-sharing (DBS) trajectory and street view images (SVIs), which serves as a representation of cyclists' preferences for street visual environments during routing. In addition, we demonstrate the feasibility and reliability of MEDIRL in discovering cyclists' street visual preferences. Further analysis reveals the nonlinear and interactive effects of street visual elements on cyclists' preferences, offering a holistic perspective on streetscape design. Our proposed framework advances the understanding of individual cycling behaviors and provides actionable insights for urban planners to design bicycle-friendly streetscapes that prioritize cyclists' preferences.