检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:傅志妍 高于越 陈坚[1,4] 陈琦[4] FU Zhi-yan;GAO Yu-yue;CHEN Jian;CHEN Qi(School of Traffic&Transportation,Chongqing Jiaotong University,Chongqing 400074,China;School of Economics and Business Administration,Chongqing University of Education,Chongqing 400067;Chongqing Transportation Planning and Research Institute,Chongqing 400074,China;Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies,Southeast University,Nanjing 211189,China)
机构地区:[1]重庆交通大学,交通运输学院,重庆400074 [2]重庆第二师范学院,经济与工商管理学院,重庆400067 [3]重庆市交通规划研究院,重庆400074 [4]东南大学,江苏省现代城市交通技术江苏高校协同创新中心,南京211189
出 处:《交通运输系统工程与信息》2023年第1期207-215,共9页Journal of Transportation Systems Engineering and Information Technology
基 金:重庆市教育委员会科学技术研究计划项目(KJQN202001611,KJZD-K202100706);四川省科技项目(2022YFH0016)。
摘 要:为揭示新冠疫情背景下公交客流量变化的空间影响因素,以疫情前后公交站点层面客流变化量为因变量,以建成环境、病毒感染情况及病毒传播途径等指标为自变量,构建新冠疫情与建成环境对公交客流量共同影响的线性回归(Ordinary Least Squares,OLS)模型与梯度提升回归树(Gradient Boosting Regression Trees,GBRT)模型。以广州市为实证对象,基于公交IC卡数据、兴趣点数据(Point of Interest,POI)及道路网络数据等多源异构数据进行模型实证分析。结果表明:考虑非线性效应的GBRT模型比OLS模型具有更好的拟合度;同时,常规公交站点的公交线路数量(22.02%)和到市中心距离(13.56%)是影响疫情背景下公交客流量变化的最重要因素,片区病毒感染与传播情况对疫情防控常态化时期的公交客流量作用有限,居民日常公交出行已经从疫情的影响下逐渐恢复。In this study,the factors of COVID-19 and the built environment are used to examine variations in bus passenger flow.The study aims to reveal the influencing mechanism of bus passenger flow in the context of epidemic prevention and control,thereby providing strategic support for the quick recovery of bus passenger flow in the postepidemic period.This study focuses on Guangzhou City,and the data are collected from the bus IC card,point of interest(POI),and road network.The ordinary least squares(OLS)model and gradient boosting regression tree model(GBRT)are constructed to analyze the passenger flow of bus stops.The results show that the fitness of the GBRT model,which takes into account nonlinear effects,is superior to that of the OLS model.The key factors influencing changes in bus passenger flow during the epidemic period are the number of bus lines(which accounts for 22.02%)and the distance to the city center(which accounts for 13.56%).The findings indicate that the impact of COVID-19 on bus passenger flow is not crucial.With the normalization of epidemic prevention and control,people’s demand for bus travel is recovering.
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.145