用逆强化学习分析骑行轨迹与街景，挖掘骑行者视觉偏好。
Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning
- 结合共享单车轨迹与街景图像，用MEDIRL模型推断骑行奖励函数。
- 发现骑行者关注安全、街道围合感和骑行舒适度等视觉要素。
- 为城市规划提供数据驱动的友好骑行环境设计参考。
骑行因健康与城市效益日益普及。早期研究虽探讨了骑行行为与环境因素的关系，但受限于数据，难以大规模刻画详细骑行过程，且忽视了骑行偏好的复杂性。为此，我们提出一种新框架，利用最大熵深度逆强化学习（MEDIRL）与可解释人工智能（XAI），量化并解析骑行者的复杂视觉偏好。在深圳市坂田片区实施，通过融合无桩共享单车轨迹（DBS）与街景图像（SVIs），构建骑行者对街道视觉环境的偏好表征。实验验证了MEDIRL在发现骑行视觉偏好方面的可行性与可靠性。结果表明，骑行者在路线选择中关注特定街道视觉元素，可归纳为对安全、街道围合感及骑行舒适度的关注。进一步分析揭示街道视觉元素对偏好的非线性复杂影响，为景观设计提供了低成本优化视角。本框架深化了对个体骑行行为的理解，为城市规划者提供以骑行者偏好为核心的友好街道设计依据。
原文摘要 · Abstract (English)
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 visual preferences 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' visual preferences. We find that cyclists focus on specific street visual elements when making route decisions, which can be summarized as their attention to safety, street enclosure, and cycling comfort. Further analysis reveals the complex nonlinear effects of street visual elements on cyclists' preferences, offering a cost-effective perspective on streetscapes 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.
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