摘要
城市绿道作为连接和优化城市生态系统的关键绿色基础设施,为居民提供了慢跑、健步走和骑行等活动的线性空间,在促进居民生理与心理健康方面发挥了积极作用。以成都环城生态区绿道为研究对象,基于连续15个月的众包运动轨迹数据(Strava),提取绿道骑行流量分布,并建立多层线性模型,探究绿道环境对骑行行为的影响机制。结果显示:1)环城生态区绿道的骑行流量在空间分布上呈现“南多北少”的格局,在时间维度上则表现出“春秋多,夏冬少”的季节性变化规律;2)与传统线性回归模型相比,多层线性模型的AIC值更低,表现更好;3)骑行流量与绿道周边的服务设施密度、土地利用混合度、绿地用地比例、出入口数量以及步行可达性均呈显著正相关。基于此,提出“需求响应:多样化的服务设施配置”“空间可达:便捷化的交通接入系统”“以线带面:共生式的绿道-公园环链”三方面的优化策略。研究结果为绿道规划与设计提供了理论依据与实践参考。
Physical activity refers to"any bodily movement produced by skeletal muscles that results in energy expenditure".It includes walking,jogging,cycling,swimming,and engaging in sports or exercise.Physical activity is essential for maintaining overall health,enhancing cardiovascular fitness,improving mental wellbeing,and reducing the risk of chronic diseases.However,as modern lifestyles have become increasingly sedentary,insufficient physical activity now represents a significant global public health concern,contributing to the prevalence of obesity and chronic diseases.Consequently,encouraging people to engage in more daily physical activity has become a central focus of current health policy initiatives.As an essential component of green infrastructure that connects and enhances urban ecosystems,urban greenways provide linear open spaces designed for recreational use,transportation,and environmental protection.They serve multiple purposes,such as promoting physical activity,enhancing biodiversity,improving air quality,and providing aesthetic and social benefits to urban residents.In recent years,many cities and regions have actively promoted the construction of urban greenways.However,many urban greenways face challenges such as poor environmental quality and inadequate route design,which adversely affect cycling activity and limit the health benefits these greenways can provide.Therefore,enhancing greenway environments to encourage cycling activity is crucial.Scholars have conducted extensive research on the associations between urban built environments and residents'cycling behavior,yielding many significant results.However,previous studies generally exhibit three key limitations.First,they primarily focus on areas such as urban settings,parks,and communities,with relatively limited attention given to linear spaces like greenways.Second,while studies are increasingly utilizing multisource data for analyzing cycling behavior,there is insufficient emphasis on opensource data provided by fitness apps that rely on crowdsourcing(e.g.,Keep and Strava).Such data can capture cycling activities on greenways in a more detailed and long-term manner.Finally,previous research predominantly employed traditional linear regression models,focusing mainly on single periods and rarely using multiple periods to analyze the spatial and temporal variations in cycling behavior.Linear regression models do not account for nested data or repeated observations,whereas multi-level models are more suitable for analyzing cycling activity data with a multi-layered structure across multiple periods.This study analyzes the spatiotemporal characteristics of cycling behavior on greenways in the Chengdu Ring Ecological Zone by obtaining crowdsourced sports trajectory data from the fitness app Strava over a consecutive 15-month period.Additionally,a multilevel linear model is developed to elucidate the effects of greenway environmental factors on the cycling volume of greenway segments.Corresponding optimization strategies for greenway environments are proposed,providing a valuable reference for urban greenway planning and construction.The main findings of this study are as follows:1)Cycling volume along the greenway exhibits a temporal and spatial disparity characterized by a greater volume in the southern region compared to the northern region,as well as a seasonal pattern where volume is higher in spring and autumn and lower in summer and winter.2)The multilevel linear model demonstrates superior performance over the traditional linear regression model,as evidenced by a lower AIC value.3)There is a significant positive correlation between cycling volume and several factors,including the density of service facilities along the greenway,land use mix,the proportion of green space,the number of entrances,and pedestrian accessibility.4)The effects of population density,distance to the ring road,proximity to Financial City(a high-end financial center),and the normalized difference vegetation index(NDVI)on cycling volume are found to be insignificant.The findings indicate that a reasonable allocation of service facilities along the greenway,the enhancement of pedestrian access systems,and the optimization of the surrounding landscape quality can contribute to an increase in cycling volume along the greenway.Through planning interventions to improve the greenway environment,we can effectively enhance its usage efficiency and health benefits.Based on the current situation of the greenways,the spatio-temporal cycling characteristics,and the multilevel modeling results,this study proposes three key optimization strategies:1)demand response:diversified service facility configurations;2)spatial accessibility:convenient transportation access systems;and 3)using lines to activate areas:a symbiotic greenway-park loop line.The results of this study provide a theoretical foundation and practical reference for the planning and design of greenways.Such considerations not only promote increased cycling activity but also contribute to broader public health objectives by encouraging physical activity.
作者
杨林川
彭迎澳
喻冰洁
杨钦然
YANG Linchuan;PENG Yingao;YU Bingjie;YANG Qinran(School of Architecture,Southwest Jiaotong University,Chengdu 611756)
出处
《中国园林》
北大核心
2025年第8期105-112,共8页
Chinese Landscape Architecture
基金
国家自然科学基金面上项目(52278080)
四川省杰出青年科学基金项目(2025NSFJQ0016)。
关键词
风景园林
建成环境
骑行行为
轨迹数据
成都环城生态区
landscape architecture
built environment
cycling behavior
trajectory data
Chengdu Ring Ecological Zone
