How Do Street Landscapes Influence Cycling Preferences? Revealing Nonlinear and Interaction Effects Using Interpretable Machine Learning: A Case Study of Xiamen Island

Pengliang Hu, Jingnan Huang, Libo Fang, Chao Luo, Ershen Zhang and Guoen Wang ()
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Pengliang Hu: School of Urban Design, Wuhan University, Wuhan 430072, China
Jingnan Huang: School of Urban Design, Wuhan University, Wuhan 430072, China
Libo Fang: Hunan Architectural Design Institute Group Co., Ltd., Changsha 410000, China
Chao Luo: School of Visual Arts Design, Hubei Institute of Fine Arts, Wuhan 430060, China
Ershen Zhang: School of Urban Design, Wuhan University, Wuhan 430072, China
Guoen Wang: School of Urban Design, Wuhan University, Wuhan 430072, China

Land, 2025, vol. 14, issue 11, 1-20

Abstract: Building cycling-friendly street environments is crucial for promoting sustainable urban mobility. However, existing studies exploring the influence of the built environment on cycling have paid limited attention to the three-dimensional characteristics of street landscapes and have mostly relied on linear assumptions. To address these gaps, this study employs street view imagery and interpretable machine learning methods to investigate the nonlinear and interaction effects of street landscape elements on residents’ cycling preferences in Xiamen Island, China. The results reveal that the visual indices of buildings, sky, vegetation, and roads are the most influential variables affecting cycling preferences. These factors exhibit pronounced nonlinear relationships with cycling preference. For instance, buildings exhibit a threshold effect, with positive influences on cycling preference when the building index is below 0.12 and negative effects when it exceeds 0.12. A low sky index significantly suppresses cycling preference, whereas higher values offer only limited additional benefits, with an optimal range of 0.1–0.25. Vegetation contributes positively only at relatively high levels, suggesting that its index should ideally exceed 0.3. The road index shows a V-shaped relationship: values between 0.15 and 0.25 reduce cycling preference, whereas values below 0.15 or above 0.25 enhance it. Moreover, clear interaction effects among these variables are observed, suggesting that the combined visual composition of the streetscape plays an important role in shaping cycling preferences. These findings deepen the understanding of how street landscape characteristics influence cycling behavior and provide nuanced, practical insights for designing cycling-friendly streets and promoting sustainable travel in urban environments.

Keywords: streetscape; subjective perception; cycling preference; nonlinearity; interaction effects (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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