第46卷第7期
2023年7月
合肥工业大学学报
JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)
Vol.46 No.7
Jul. 2023

DOI:10.3969/j.issn.1003-5060.2023.07.016

基于 GWR 的建成环境对公共自行车出行模式的影响分析

王涛 $ ^{1,2} $,戢晓峰 $ ^{1} $

(1. 昆明理工大学交通工程学院,云南昆明 650504;2. 重庆市交通规划研究院,重庆 401120)

摘要

为分析建成环境对公共自行车出行模式的影响,文章结合公共自行车运营数据和建成环境数据,以公共自行车站点为中心建立缓冲区并提取缓冲区内兴趣点(point of interest, POI),在考虑POI规模的基础上划分站点类型;根据站点类型对出行起讫点(origin-destination, OD)分类,以OD类型确定公共自行车出行模式,使用地理加权回归(geographically weighted regression, GWR)模型,分析建成环境对公共自行车出行模式的影响;以昆明市为例进行实证分析。结果表明:昆明市公共自行车出行模式可划分为16种,OD皆为住宅主导型和公司(企业)主导型站点的出行模式约占69.26%;建成环境对不同出行模式的影响效应存在差异;土地利用混合度是公共自行车出行模式的主要影响因素。研究结果可为公共自行车布局优化及运营管理提供参考。

关键词

城市交通;公共自行车;出行模式;地理加权回归(GWR);建成环境

中图分类号:U484

文献标志码:A

文章编号:1003-5060(2023)07-0963-09

Impact of built environment on public bicycle travel patterns based on geographically weighted regression

WANG Tao $ ^{1,2} $, JI Xiaofeng $ ^{1} $

(1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China; 2. Chongqing Transport Planning Institute, Chongqing 401120, China)

Abstract

This paper explores the impact of the built environment on the travel patterns of public bicycles. Combined with the public bicycle usage data and built environment data, a buffer zone centered on public bicycle station was established, and the points of interest (POIs) in it were extracted. Based on the scale differences of POIs, the station type was divided. Then the origin-destination (OD) data of public bicycle was classified according to the types of stations. And the public bicycle travel patterns were defined by OD properties. Finally, geographically weighted regression (GWR) model was used to analyze the impact of the built environment on public bicycle travel patterns. An empirical study on Kunming City was conducted, and the results show that there are 16 public bicycle travel patterns in Kunming City, and the proportion of travel patterns that both OD are residential-led and company-led stations is 69.26%; the built environment has different effects for different travel patterns; land use mix is the chief factor influencing public bicycle travel patterns. The research results can provide reference for the optimization of public bicycle layout and operations management.

Keywords

urban traffic; public bicycle; travel pattern; geographically weighted regression(GWR); built environment

收稿日期:2021-10-26

修回日期:2022-07-24

基金项目:国家自然科学基金资助项目(42061030);云南省交通运输厅科技创新及示范资助项目(2021-86-4)