基于时空序列相似性的城轨乘客出行模式识别

doi:10.13229/j.cnki.jdxbgxb.20221413

摘要/Abstract

摘要：

基于轨道交通智能卡数据，提出一种通过建模个体的时空序列识别出行模式的方法。首先，提取乘客个体访问的所有站点，以站间出行频次、站间距离和站点活动时长计算站点的相似性，利用层次聚类算法划分该个体的主要空间活动区域。其次，基于个体的出行次序推断时空序列，该序列为一组表征时空状态的离散值，依次采用PCA-KL和K-Means++提取相似性序列结构以识别乘客出行模式。最后，以西安某月的轨道交通智能卡数据为例，识别其乘客出行模式。结果表明，复杂的客流具有5种出行模式，其中3种典型模式宏观上属通勤出行，客流占比79%。可见，本文基于个体时空序列相似性的模式识别充分体现了研究方法的特殊性和通用性，针对不同城市操作性强。

关键词: 交通运输系统工程, 城市轨道交通, 智能卡数据, 时空序列, 乘客出行模式, 通勤出行

Abstract:

Based on smart card data （SCD） of urban rail transit， a method was proposed to identify travel patterns by modeling the spatio-temporal sequences of individuals. Firstly， all stations visited by a passenger individual were extracted， and the similarity of stations was calculated in terms of inter-station travel frequency， inter-station distance， and the station activity duration， thus the main spatial activity areas of this individual were classified using a hierarchical clustering algorithm. Secondly， the spatio-temporal sequence was inferred based on the travel order of the individual， which is a set of discrete values characterizing the spatio-temporal state. PCA-KL and K-Means++ were used to extract the similarity sequence structure to identify passenger travel patterns. Finally， using one-month SCD as an example to identify passenger travel patterns for Xi'an rail transit. The results show that the complex passenger flow has five travel patterns， among which three typical travel patterns are macroscopic commuter travel behavior， accounting for 79% of the total passenger flow. Thus， the pattern identified based on the similarity of individuals' travel spatiotemporal sequences fully reflects the particularity and universality of the research method and it is highly operable for different cities.

Key words: transportation system engineering, urban rail transit, smart card data, spatiotemporal sequence, passenger travel pattern, commuting travel

中图分类号:

U491.1

张娜,陈峰,王剑坡,朱亚迪. 基于时空序列相似性的城轨乘客出行模式识别[J]. 吉林大学学报(工学版), 2024, 54(9): 2588-2599.

Na ZHANG,Feng CHEN,Jian-po WANG,Ya-di ZHU. Recognition of travel patterns for urban rail transit passengers based on spatiotemporal sequence similarity[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2588-2599.

图/表 15

表1

图1

图2

图3

图4

图5

图6

表2

不同空间区域平均活动时长"

项目	空间区域状态编号								其他状态编号
项目	1	2	3	4	5	6	7	…	0	-1	-2
平均 $τ$ /%	37.3	16.5	5.7	4.1	3.5	0.9	0.6	…	-	29.4	-

表2

图7

表3

图8

图9

图10

图11

表4

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交易时间	票卡ID	进站时间	进站ID	出站时间	出站ID
20181112	710001***	142215	261	144134	249
20181112	710001***	173457	333	175812	261
20181113	710001***	081228	261	083526	245
20181113	710001***	144350	245	150500	261
20181116	710001***	114607	261	121415	241
20181116	710001***	173339	241	175452	227
20181216	710001***	145417	261	151257	249
20181216	710001***	171938	249	173958	261

指标	K值
指标	2	3	4	5	6	7	8	9	10
DBI	3.06	2.619	2.324	2.164	2.101	2.021	1.948	1.935	1.816
CH	0.091	0.109	0.113	0.120	0.118	0.117	0.116	0.113	0.114
K值	11	12	13	14	15	16	17	18	19
DBI	1.927	1.892	1.886	1.866	1.873	1.881	1.853	1.842	1.851
CH	0.114	0.112	0.105	0.107	0.108	0.102	0.102	0.101	0.099

类别	名称	构成集群	乘客量占比/%	出行量占比/%	时空特征	出行特征总体描述
模式1	固定时空强化出行	1、4	18.5	21.5	出行频次最高、时间稳定，典型居住地、工作地	属通勤行为
模式2	固定时空常规出行	3、7、8	35.8	41.8	出行频次稳定、时间稳定，典型居住地、工作地	属通勤行为
模式3	弹性时间无典型工作地出行	2、10	16.7	15.7	出行频次不稳定、时间灵活；典型居住地、无典型工作地	属通勤行为
模式4	偶发性无规则出行	5、6	11.6	11.7	城轨辅助出行，无法判断	非通勤行为
模式5	随机时空出行	9	17.4	9.3	出行频次与时间均随机；无典型居住地、工作地	非通勤行为