摘要
以成都市为例,基于地铁刷卡、POI、建成环境等多源大数据,运用GIS、Python、Spark等进行数据处理,分析156个站点客流量、出行时间的空间分异特征,并通过梯度提升决策树(GBDT),解析站域设施数量、建成环境、经济属性等15个因素对客流量与出行时间的非线性影响机制。研究发现:①成都市轨道交通客流量具有中间高—两边低的倒U型分布特点,每站日均客流量为1.6万人次;②出行时间随距CBD距离增加逐渐增大,每站乘客平均出行时间为32 min,出行时间的概率密度呈Gamma分布特点;③路网密度、容积率、办公设施、交通设施等对客流量具有非线性正向影响,而房价、距CBD距离与客流量则分别为“凸”形与“凹”形的非线性关系;④距CBD距离、购物数量、办公数量等与出行时间非线性正相关,而交通设施、房价等与其负相关。最后,提出考虑“流量”与“时间”阈值效应的建成环境优化、构建“出行+生活”的轨道通勤圈等规划策略。
Taking the metro system of Chengdu as an example,this paper used multi-source big/open data(e.g.,bus swipe,POI,and built environment data)and investigated the spatio-temporal characteristics of metro ridership and travel time.The nonlinear influence and the threshold effect of the number of facilities,built environment,economic attribute on metro ridership and travel time have been scrutinized by gradient boost decision tree(GBDT).It's found that:1)The metro ridership distribution in Chengdu exhibited an inverted"U"pattern,with an average daily passenger flow of 16,000 per station.2)The travel time increased with advancing distance from/to CBD,and the average travel time is 32 minutes.The hourly passenger flow follows the gamma distribution.3)The density of the road network,floor area ratio,number of office facilities,and number of traffic facilities have nonlinear positive influences on passenger flow,while the influences of house prices and distance from CBD on metro ridership are convex-shaped and concave-shaped,respectively.4)Distance from CBD,number of shopping opportunities,and number of office opportunities have nonlinear positive correlations with travel time,while the negative correlation has been observed between traffic facilities,house prices,and travel time.At last,it proposed improvement strategies for the optimization of the built environment with joint consideration of"metro ridership"and"time",and the construction of a rail commuting circle of"trip+life".
作者
崔叙
喻冰洁
杨林川
梁源
张凌菲
方翰
CUI Xu;YU Bingjie;YANG Linchuan;LIANG Yuan;ZHANG Lingfei;FANG Han(School of Architecture and Design,Southwest Jiaotong University,Chengdu 611756,Sichuan,China;Urban Mobility Institute,Tongji University,Shanghai 201800,China;School of Architecture and Urban Planning,Chongqing University,Chongqing 400030,China)
出处
《经济地理》
CSSCI
CSCD
北大核心
2021年第7期61-72,共12页
Economic Geography
基金
国家自然科学基金区域创新发展联合基金重点支持项目(U20A20330)
国家自然科学基金项目(51778530)
西南交通大学2019年博士创新基金项目(2017310253)。
关键词
城市轨道交通
客流量
出行时间
梯度提升决策树
轨道通勤圈
路网密度
房价
urban rail transit
passenger flow
travel time
gradient boosting decision tree
rail commuter circle
road network density
housing price
