摘要
为研究共享单车使用特征与建成环境的依赖关系,本文以2020年厦门市共享单车订单数据和电子围栏信息为基础,从集计(栅格区域)和非集计(个体出行)两个层面,利用极端梯度提升模型(XGboost),探究建成环境对共享单车使用特征的非线性解释能力。首先,识别密度、设计、目的地可达性、土地利用多样性、公共交通可达性和需求管理六个维度的建成环境变量对单车出行生成、吸引以及用户出发时间选择的相对重要性。之后,根据部分依赖图,揭示建成环境变量对单车使用特征指标的影响趋势。结果表明,在集计层面,电子围栏密度是最重要的建成环境因素,对出行生成和吸引的影响程度分别为26.88%和51.90%,且在150个·km^(-2)附近产生阈值效应。在非集计层面,单车用户早高峰借车概率与出行起讫点的建成环境均有关联。其中,目的地栅格中工作场所比例影响最显著(18.17%),出发地栅格CBD邻近度(7.34%)和出发地栅格公交站点密度(5.91%)次之。
To investigate the dependency of dockless bike usage characteristics on the built environment,this paper used dockless bike order data and electronic fence information from Xiamen city in 2020 to analyze the nonlinear explanatory power of the built environment at both the aggregated(grid-level)and disaggregated(individual trips)levels.A machine learning model,namely,extreme gradient boosting model(XGBoost)was adopted.First,the relative importance of six dimensions of built environment variables(density,design,destination accessibility,land use diversity,public transport accessibility,and demand management)were identified on bike trip generation,attraction,and the user's departure time choice.Then,according to partial dependence plots,the impact trends and the threshold effects of built environment variables were evaluated.The results revealed that at the aggregate level,electronic fence density was the most critical factor,affecting travel generation and attraction by 26.88%and 51.88%respectively.A threshold effect was approximately 150 per·km^(-2).At the disaggregate level,the probability of dockless bike users borrowing bikes during the morning peak was associated with the built environment features of both the origins and destinations.Among these,the proportion of workplaces in the destination grid was the most significant factor(18.17%),followed by the proximity to Central Business District(CBD)of the origin grid(7.34%)and bus stop density in the origin grid(5.91%).
作者
陈奕璠
张步镐
党振
郭唐仪
顾子渊
张玉梁
CHEN Yifan;ZHANG Buhao;DANG Zhen;GUO Tangyi;GU Ziyuan;ZHANG Yuliang(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China;Zhejiang Intelligent Transportation Engineering Technology Research Center,Enjoyer Technology Co Ltd,Hangzhou 310040,China;School of Transportation,Southeast University,Nanjing 211189,China;Institute of Intelligent Transportation Systems,Zhejiang University,Hangzhou 310058,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2024年第2期217-224,共8页
Journal of Transportation Systems Engineering and Information Technology
基金
国家重点研发计划(2019YFE0123800)
浙江省智能交通工程技术研究中心开放课题(2023ERCITZJ-KF11)
江苏省重大科技示范项目(BE2022860)。
