Visual Environment Factors Influencing Staying Behaviors During Cycling on Countryside Greenways
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WEN Ting is a master student in the Institute of Landscape Architecture, College of Agriculture and Biotechnology, Zhejiang University. Her research focuses on landscape planning and design |
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ZHANG Yun, Ph.D., is an associate professor in the Institute of Landscape Architecture, College of Agriculture and Biotechnology, Zhejiang University. Her research focuses on landscape planning and design |
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DU Ming, Ph.D., is an associate professor in the Faculty of Civil Engineering and Architecture, Zhejiang Sci-Tech University. His research focuses on landscape heritage conservation and historic place regeneration |
Received date: 2024-05-20
Revised date: 2024-12-23
Online published: 2025-12-07
Copyright
[Objective] Countryside greenways represent an organic extension of urban green slow-moving traffic systems into suburban areas, addressing urban residents' demands for closer contact with nature and fostering the development of an integrated urban-rural belt. Cycling, as a slow-moving activity that combines outdoor recreation and leisure tourism, constitutes one of the primary modes of activity on countryside greenways. Compared to urban greenways, cycling on countryside greenways is characterized by faster speeds, longer distances, and longer durations, causing cyclists to pay closer attention to the amenities, scenery, and safety aspects of the staying spaces when selecting a site and determining the duration of their sojourn. Since visual perception accounts for 80%-90% of the information humans receive from their environment, it exerts a profound influence on human behavior and environmental experiences. Riders on countryside greenways are often attracted to the distinctive visual environments and tend to make spontaneous stops, which could increase safety risks. Furthermore, existing regulatory frameworks have not sufficiently addressed the safety concerns of cycling on countryside greenways, resulting in insufficient attention to this issue. Therefore, investigating the characteristics of staying behaviors and the influencing factors of the visual environment on countryside greenway cycling holds practical significance and expands upon existing greenway research. The planning and design of staying points in conjunction with the visual environment not only help improve the safety and user-friendliness of greenway cycling but also leverage scenic resources to entice cyclists to linger longer, thereby enhancing the usage intensity of the countryside greenway and further promoting the development of the rural tourism economy.
[Methods] This research focuses on the Qingshan Lake Greenway in Hangzhou. This greenway, spanning across urban built-up areas, rural settlements, and natural scenic zones, showcases a unique combination of lakeside and mountainous landscapes, making it a representative example of countryside greenways. Through field research and analysis of cycling trajectory data, local staying spaces are categorized into nodes and road sections. Four key indicators — number of stays, duration of stays, frequency of stays, and staying rate — are established to provide a comprehensive understanding of user engagement and interaction with the greenway environment. This approach allows for a more nuanced analysis of how cyclists utilize and experience the greenway spaces. Additionally, visual environmental features are analyzed through image semantic segmentation and ArcGIS, with a view to exploring their impact on staying behaviors during cycling. Correlation and multiple regression analyses are employed to determine the intensity of the visual environment’s influence on staying behaviors.
[Results] The findings reveal that cyclists tend to stay longer at nodes compared to road sections. However, the overall staying time remains brief, averaging less than one minute. This indicates low utilization rates of greenway stations and viewing platforms. Conversely, the frequency of spontaneous stays on road sections is higher, suggesting that cyclists are more inclined to make impromptu stays during their rides. Given the prevalence of cycling accidents on countryside greenways, these spontaneous stays may pose significant safety risks. Moreover, the visual environment’s influence varies between nodes and road sections; staying behaviors at nodes are primarily affected by the visual field area, while those on road sections are influenced by green visibility, with higher greenery levels promoting impromptu short stays.
[Conclusion] This research endeavors to develop spatial strategies for enhancing the staying behaviors of cyclists on countryside greenways, with a view to improving the usage intensity of greenway nodes and ensuring the safety of road segment traversal. Particular emphasis is placed on the optimization of staying spaces situated in poor visual environments, because the visibility of prominent landscape features, such as water bodies and mountain vistas, has been found to exert a significant influence on cyclists' propensity to linger. At the node level, strategies should focus on broadening the visual field and prolonging the duration of stays. This can be achieved through strategic optimization of plant layouts and reduction of physical obstructions, which collectively serve to enhance the perceivability of the surrounding natural scenery. In contrast, the spatial design of road segment staying spaces must prioritize safety considerations, especially in areas constrained by adjacent mountains or cliffs. By expanding the width of select road sections and introducing cantilevered structures or recessed micro-terraces along them, the creation of small yet safe “pocket” staying spaces can provide cyclists with suitable rest areas without compromising the overall flow of traffic. This, in turn, contributes to a more vibrant and active greenway network.The systematic integration of the spatio-temporal distribution patterns of cyclists' staying behaviors with the influencing factors of the visual environment laid the foundation for this research, which not only informs the enhancement of greenway station usage intensity and cycling safety, but also provides a robust methodological framework for the optimization of small and micro staying point placement along road segments. Looking ahead, further investigations incorporating other greenway usage characteristics, such as cycling distances and incident-prone locations, could shed additional light on the construction of cyclist-friendly countryside greenway environments that cater to the diverse needs and behavioral patterns of users.
Ting WEN , Yun ZHANG , Ming DU . Visual Environment Factors Influencing Staying Behaviors During Cycling on Countryside Greenways[J]. Landscape Architecture, 2025 , 32(2) : 95 -101 . DOI: 10.3724/j.fjyl.202405200276
表1 郊野绿道视觉环境要素指标体系计算方法Tab. 1 Computational methods for visual envrionment indicators of countryside greenways |
| 计算工具 | 要素指标 | 计算方法 |
| ArcGIS | 视域面积 | 视域分析工具计算观察点的视域面积 |
| 图像语义分割 | 蓝色视野指数 | 静态图像分割得到水域占观测图像的像素面积比值 |
| 山体面积占比 | 静态图像分割得到山体占观测图像的像素面积比值 | |
| 绿视率 | 静态图像分割得到绿植占观测图像的像素面积比值 | |
| 道路面积占比 | 静态图像分割得到道路占观测图像的像素面积比值 | |
| 建筑面积占比 | 静态图像分割得到建筑占观测图像的像素面积比值 |
表2 视觉环境要素与停驻行为相关性Tab. 2 Correlation of visual environment elements and staying behaviors |
| 空间类型 | 指标因子 | 停驻人数 | 停驻时间 | 停驻人次 | 停驻率 |
| 注:**在0.01级别(双尾),相关性显著;*在0.05级别(双尾)相关性显著。 | |||||
| 节点 | 视域面积 | 0.778** | 0.797** | ||
| 蓝色视野指数 | 0.753** | 0.739** | |||
| 山体面积占比 | 0.658** | 0.764** | |||
| 路段 | 绿视率 | 0.686** | 0.652** | ||
| 道路面积占比 | 0.302* | 0.219* | |||
| 建筑面积占比 | -0.469** | -0.469** | |||
表3 影响停驻行为的视觉环境显著因子Tab. 3 Salience visual environment factors of staying behavior |
| 空间类型 | 因变量 | D-W值 | 调整后的R2 | 模型系数 | 标准化系数Beta | t检验 |
| 注:**在0.01级别(双尾),相关性显著;*在0.05级别(双尾),相关性显著。 | ||||||
| 节点 | 停驻人数 | 1.771 | 0.662 | 常量 | 1.022 | |
| 视域面积 | 0.440** | 2.604 | ||||
| 蓝色视野指数 | 0.357** | 2.131 | ||||
| 山体面积占比 | 0.133* | 0.832 | ||||
| 停驻时间 | 2.071 | 0.728 | 常量 | -0.087 | ||
| 视域面积 | 0.412** | 2.717 | ||||
| 蓝色视野指数 | 0.225* | 1.494 | ||||
| 山体面积占比 | 0.345** | 2.409 | ||||
| 路段 | 停驻人次 | 1.633 | 0.598 | 常量 | -0.144 | |
| 绿视率 | 0.570** | 4.557 | ||||
| 道路面积占比 | 0.344** | 2.935 | ||||
| 建筑面积占比 | -0.304** | -2.407 | ||||
| 停驻率 | 1.668 | 0.501 | 常量 | 1.318 | ||
| 绿视率 | 0.537** | 3.847 | ||||
| 道路面积占比 | 0.261** | 2.001 | ||||
| 建筑面积占比 | -0.305** | -2.167 | ||||
文中图表均由作者绘制或拍摄,其中
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