摘要
新冠疫情的爆发对城市居民生活造成显著影响,研究疫情背景下居民出行行为的变化规律,对于公共交通系统规划运营具有重要意义。利用非负张量分解方法对2020年新冠疫情前后各一周的北京市地铁交通数据进行分析,得到早、晚通勤和日常3种出行模式,并对各出行模式疫情前后的时空变化进行了对比。研究表明,疫情后日常出行模式基本时间特征变化较大,而工作日通勤出行模式基本时间特征变化较小;以商业区为目标站点的出行在日期和时段选择上均呈现错峰特点。
The outbreak of COVID-19 has had a significant impact on the lives of urban residents.It is of great significance for the planning and operation of public transportation system to study the change rules of residents’travel behavior under the background of epidemic.We used the nonnegative tensor factorization method to analyze the Beijing subway traffic data for one week before and after the COVID-19 pandemic in 2020,obtained three travel modes,including morning,evening commuting and daily,and analyzed the spatio-temporal changes of several travel modes before and after the pandemic.The results show that after the pandemic,the basic time characteristics of daily travel mode have changed greatly,while the basic time characteristics of commuting travel modes on weekdays have not changed much.The travel with the commercial area as the target station presents the characteristics of staggered peak in the choice of date and time period.
作者
刘成帅
顾海硕
陈鹏
LIU Chengshuai;GU Haishuo;CHEN Peng(School for Informatics and Cyber Security,People’s Public Security University of China,Beijing 100038,China;Key Laboratory of Security Technology&Risk Assessment,Ministry of Public Security,Beijing 102600,China)
出处
《地理空间信息》
2024年第9期13-18,共6页
Geospatial Information
基金
北京市优质本科课程——犯罪活动空间分析与制图
北京市自然科学基金面上基金资助项目(9192022)。
关键词
城市交通
新冠疫情
非负张量分解
时空特征
地铁出行
urban traffic
COVID-19
nonnegative tensor factorization
spatio-temporal characteristic
subway travel
