联合重复性与周期性的公交乘客个体出行规律分类

A Segmentation of Bus Passenger Combining Repeatability and Periodicity of Individual Travel Pattern

针对分别度量个体出行短期规律(重复性)与长期规律(周期性)信息表征不完全问题,本文提出联合重复性与周期性度量公交乘客出行规律,按个体日出行链划分乘客出行模式,分别改进重复性信息熵模型与周期性评分函数,基于K-Means++算法对公交乘客出行规律进行分类。应用2019年10月~12月浙江省海宁市的数据,从时间、空间两个维度分别度量重复性与周期性,得到4个规律程度的信息熵指标;用仅重复性以及联合重复性与周期性两种方法,分别将71080名公交乘客分为重复性强且周期性强、重复性强但周期性弱、重复性弱且周期性弱这3类。比较两种方法下3类乘客出行时间的频数分布与个体数量变化发现,联合重复性与周期性可以使30.52%,即21692名乘客的类别划分更加合理;比较乘客个体出行模式热力图,证明了方法的有效性。结果表明:联合重复性与周期性度量乘客个体出行规律,可以提升公交个性化需求的识别精准度。

Existing literature on the regularity of bus passengers'travel behavior has primarily focused on measuring repeatability and periodicity separately.However,it is recognized that repeatability and periodicity coexist in the travel patterns of individuals.To address this,this paper proposes a method that combines repeatability and periodicity to measure the regularity of bus passengers.Daily activity sequential chains of bus passengers are utilized to identify individual travel patterns.And the information entropy model is improved to measure repeatability and the periodic detection function is improved to measure periodicity.By combining these measures,a K-means++clustering method is developed for measuring the regularity of bus passengers.To evaluate the method,we obtain data from October to December 2019 in Haining City,Zhejiang Province,China.Four information entropy indices are presented to measure regularity,including temporal repeatability,spatial repeatability,temporal periodicity,and spatial periodicity.A total of 71080 bus passengers were classified into three groups featured with high repeatability and periodicity,low repeatability and high periodicity,low repeatability and periodicity.And we compare the frequency distribution of bus passengers with different departure times across the three groups.The results show that by combining repeatability and periodicity,an additional 21692 passengers,which account for 30.52%of all passengers,can be classified into an interpretable group.We also provide visualizations of travel patterns for the most regular passengers in each group,further illustrating the performance of the proposed measure.This paper demonstrates that combining repeatability and periodicity is a valuable approach for accurately identifying and understanding the individual travel demand of bus passengers.