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URBAN-SPIN:一种用于历史城市中心设计实施的街道级自行车适应性指数

URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres

Cambridge Cognitive Architecture, Department of Architecture, University of Cambridge, UK(剑桥认知架构,建筑系,剑桥大学,英国);NeuroCivitas Lab for NeuroArchitecture, Centre for Research in the Arts, Social Sciences and Humanities (CRASSH), University of Cambridge, UK(神经文明架构实验室,艺术、社会科学与人文研究中心(CRASSH),剑桥大学,英国)

Haining Ding, Chenxi Wang, Simon Ladouce, Michal Gath-Morad

AI总结:

本研究提出一种基于类型学和数据整合的框架,通过分析街道类型和子分类,构建整合主观和感知维度的自行车适应性指数,以评估和改善历史城市中的骑行条件。

摘要:

自行车在28个国家中,有平均35%的成年人每周至少骑行一次,作为易受周围环境影响的弱势道路使用者,骑行者对街道的体验强度远高于其他交通方式。然而,塑造这一体验的街道级特征仍缺乏深入分析,特别是在历史城市环境中,空间限制使得大规模基础设施变革难以实现,且类型学背景常被忽视。本研究开发了一种以感知为导向、基于类型学和数据整合的框架,明确建模街道类型及其子分类,以评估视觉和空间配置如何影响骑行体验。基于本研究开发的剑桥骑行体验视频数据集(CCEVD),一个第一人称和车把-mounted数据集,我们利用计算机视觉提取细粒度的街道景观指标,并将其与建筑环境变量和主观评分相结合,从而构建一种类型学敏感的自行车适应性指数,整合主观和感知维度与物理指标,用于段级比较。统计分析显示,感知的自行车适应性源于特征之间累积、情境特定的相互作用。尽管绿化和开放性一致提升舒适度和愉悦感,但封闭性、可视性和建筑连续性则显示阈值或差异效应,取决于街道类型和子类型。AI辅助的视觉重新设计进一步表明,细微、有针对性的改动可以带来有意义的感知提升,而无需大规模的结构性干预。该框架提供了一种可转移的模型,通过感知导向、类型学敏感的设计策略,评估和改善历史城市中的骑行条件。

英文摘要:

Cycling is reported by an average of 35% of adults at least once per week across 28 countries, and as vulnerable road users directly exposed to their surroundings, cyclists experience the street at an intensity unmatched by other modes. Yet the street-level features that shape this experience remain under-analysed, particularly in historical urban contexts where spatial constraints rule out large-scale infrastructural change and where typological context is often overlooked. This study develops a perception-led, typology-based, and data-integrated framework that explicitly models street typologies and their sub-classifications to evaluate how visual and spatial configurations shape cycling experience. Drawing on the Cambridge Cycling Experience Video Dataset (CCEVD), a first-person and handlebar-mounted corpus developed in this study, we extract fine-grained streetscape indicators with computer vision and pair them with built-environment variables and subjective ratings from a Balanced Incomplete Block Design (BIBD) survey, thereby constructing a typology-sensitive Bikeability Index that integrates subjective and perceived dimensions with physical metrics for segment-level comparison. Statistical analysis shows that perceived bikeability arises from cumulative, context-specific interactions among features. While greenness and openness consistently enhance comfort and pleasure, enclosure, imageability, and building continuity display threshold or divergent effects contingent on street type and subtype. AI-assisted visual redesigns further demonstrate that subtle, targeted changes can yield meaningful perceptual gains without large-scale structural interventions. The framework offers a transferable model for evaluating and improving cycling conditions in heritage cities through perceptually attuned, typology-aware design strategies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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