日常高频步行街道筛选机制及其品质匹配度研究——以深圳市为例

Research on the Selection Mechanism and Quality Matching System of Frequently-Used Pedestrian Streets:Taking Shenzhen as an Example

街道作为城市生活重要公共空间近年来受到越来越多的重视。然而城市街道数量庞大,一视同仁地提升街道品质固然理想,但却并不一定是资源最佳的配置方式。因此,如何从人本主义视角挖掘高频步行街道并对其进行品质匹配度评价成为链接街道品质研究与更新实践的重要论题。然而,当前街道分级机制主要基于机动车行驶属性,而步行空间品质研究侧重客观环境设施,街道自下而上的人群使用频率被大多数研究所忽视。脱离人群使用频率的高品质建成环境是低效的,脱离了高品质建成环境的高频率人群使用是失能的,只有环境供给和人群使用需求双向平衡才能达到高效高质高能。因此,反思自上而下的静态道路分级体系,从城市群体行动规律、空间规律及移动规律三个维度自下而上地构建城市日常步行高频步行街道的筛选机制,并结合现有品质量化研究,提出人(使用)-事(设施)-场(环境)三位一体的PEP街道匹配度评价指标体系。以深圳市为例,基于多源大数据对全域约17万街道进行实时高频步行街道筛选及PEP定量测度及匹配度评价。结果表明:深圳全域街道呈现出匹配度极不均衡的情况;在约17万街道中,高频步行街道约1万条,其中环境及设施较好的仅1.53%,而环境及设施水平都呈现较低水平的街道占比超过60%;高频步行街道直接关系到人们日常的城市体验感和幸福感,值得城市学者及决策者的重视。高频步行街道筛选机制及PEP评价体系探索不确定城市发展中个体步行使用的确定关系,为当前大规模步行友好城市建设提供新思路和新方法。

Streets play a crucial role as important public spaces in urban areas,and there has been a growing focus on their improvements in recent years.However,allocating resources to uniformly improve all streets may not be the most efficient approach due to their large numbers.Therefore,it is essential to identify frequently used pedestrian streets and assess their qualities from a pedestrian's perspective.This will help to bridge the gap between street quality research and urban renewal practices,ensuring that resources are allocated effectively.The current street classification mechanisms predominantly prioritize motor vehicle attributes,while research on street quality tends to focus more on objective environmental facilities,often neglecting the analysis of pedestrian street usage patterns.This study rethinks the effectiveness of the existing top-down static road classification system and suggests a alternative dynamic and bottom-up selection mechanism for frequently used streets.This mechanism takes into account various factors including urban crowd mobility patterns,spatial patterns,and traffic flow dimensions.To address this issue,the study has developed the People-Environment-Program(PEP)street-matching evaluation index system,which integrates street usage patterns and street quality evaluation.By utilizing multi-source big data and conducting PEP quantitative measurements,the proposed mechanism was applied to evaluate approximately 170,000 streets in a case study conducted in Shenzhen.The research findings reveal a significant disparity between the quality and usage patterns of streets in Shenzhen.Specifically,the study observed that only 1.53%of frequently used streets have appropriate environmental conditions and programs,while over 60%of frequently used streets lack adequate conditions in terms of both environmental factors and programs.Frequently used streets hold significant influence over individuals'everyday experiences,highlighting the need for attention from both urban scholars and policymakers.