新冠疫情及建成环境对公交客流量的影响模型

doi:10.16097/j.cnki.1009-6744.2023.01.022

交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (1): 207-215.DOI: 10.16097/j.cnki.1009-6744.2023.01.022

新冠疫情及建成环境对公交客流量的影响模型

傅志妍^1,2，高于越³，陈坚^*1,4，陈琦⁴

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

收稿日期:2022-08-18 修回日期:2022-11-14 接受日期:2022-11-21 出版日期:2023-02-25 发布日期:2023-02-16
作者简介:傅志妍(1984- )，女，山西太原人，讲师，博士生。
基金资助:
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)

Impact Model of COVID-19 and Built Environment on Bus Passenger Flow

FU Zhi-yan^1,2, GAO Yu-yue³, CHEN Jian^*1,4, CHEN Qi⁴

1. School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China; 2. School of Economics and Business Administration, Chongqing University of Education, Chongqing 400067; 3. Chongqing Transportation Planning and Research Institute, Chongqing 400074, China; 4. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China

Received:2022-08-18 Revised:2022-11-14 Accepted:2022-11-21 Online:2023-02-25 Published:2023-02-16
Supported by:
The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202001611, KJZD-K202100706)；Sichuan Science and Technology Program (2022YFH0016)

摘要/Abstract

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。

关键词: 城市交通, 非线性效应, 梯度提升回归树, 公交客流, 新冠疫情

Abstract: 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.

Key words: urban traffic, nonlinear effect, gradient boosting regression trees, passenger flow, COVID-19

中图分类号:

U121

傅志妍, 高于越, 陈坚, 陈琦. 新冠疫情及建成环境对公交客流量的影响模型[J]. 交通运输系统工程与信息, 2023, 23(1): 207-215.

FU Zhi-yan, GAO Yu-yue, CHEN Jian, CHEN Qi. Impact Model of COVID-19 and Built Environment on Bus Passenger Flow[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 207-215.

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新冠疫情及建成环境对公交客流量的影响模型

Impact Model of COVID-19 and Built Environment on Bus Passenger Flow

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