基于个体出行模式的公交乘客活动规律性度量

Measurement of public transport passenger behavior regularity based on individual travel pattern

针对分别度量公交乘客出行重复性与周期性造成的个体活动规律特征表征不完全问题,以空间维度为例对个体活动相似性进行公交站点聚类,按乘客日活动先后顺序进行排序,构建了个体日出行链,将相同站点集群的日出行链归为同一类出行模式.分别改进了信息熵(Information Entropy,IE)模型和环形周期判别法(Ring-like Periodic Detection Method,RPDM),建立了公交乘客活动规律组合度量模型,并结合浙江省海宁市2019年10月至12月共92 d的公交出行数据进行了方法应用.比较了83423名乘客的出行活动重复性、周期性和规律性值的差异,选取不同重复性与周期性的4名乘客,分析了其92 d活动空间特征,绘制了50名出行最规律和最不规律乘客的出行模式热图,发现按个体出行模式综合度量公交乘客出行活动规律性,能区分出12.13%重复性相同而周期性不同的乘客和25.14%周期性相同而重复性不同的乘客,将乘客细分效果提升了37.06%.结果表明:基于个体出行模式重复性与周期性的组合度量模型进行公交乘客出行规律判别效果好,有利于提升公交乘客细分的精确度.

Regularity of bus passenger behavior is represented insufficiently as the repeatability and periodicity are measured separately in the existing literatures.To address this problem,this paper clusters the spatial aggregations of passenger activities according to the similarity of bus stop location.Individual daily travel chains are constructed by sorting passengers in the order of their daily activities,and the daily travel chains of the same stop clusters are grouped into the same class of travel patterns.An integrated measurement method for public transport passenger behavior regularity is presented combing the improved Information Entropy(IE)model with an improved Ring-like Periodic Detection Method(RPDM).The proposed method is applied to passenger segmentation with bus travel data during 92 days from October to December 2019 in Haining,Zhejiang Province,China.The repeatability and periodicity for travel activities of 83,423 passengers are compared.Four passengers with different repetition and periodicity are selected to analyze the spatial characteristics of their 92 days activities,and travel patterns between the most 50 regular passengers and the most 50 irregular passengers are visualized.It is found that the integrated measurement method can distinguish 37.06%more passengers than that the repeatability and periodicity are measured separately,which include passengers labelled with the same repeatability but different periodicity(12.13%)and passengers with the same periodicity but different regularity(25.14%).The results indicate that the integrated measurement of regularity combining repeatability with periodicity is an effective approach to improve the accuracy of segmentation of bus passengers.