基于关联规则的公共交通通勤稳定性人群辨识

doi:10.13229/j.cnki.jdxbgxb20180351

摘要/Abstract

摘要：

通过对北京市公共交通乘客出行行为调查数据、公共交通刷卡数据和线站数据进行关联匹配，提取了公共交通通勤乘客出行链。利用一个月的公共交通出行数据，从活动点、出行空间和出行时间的角度提取了非家活动点类别数、典型出行链占比、出行空间均衡度、时间稳定性和时间集中度5类指标用来描述乘客出行稳定性。引入关联规则方法中FP-growth算法，采用支持度、置信度和提升度3个参数挖掘不同项集长度下各特征属性之间的关联规则，识别出3类稳定性差异显著的通勤人群，并对辨识方法的合理性进行了验证。研究为制定针对性和差别化的公交供需管理策略提供了支撑，助力更加高效和精细化的公共交通出行服务。

关键词: 交通运输系统工程, 通勤乘客, 稳定性判别, 关联规则

Abstract:

To better understand the characteristics of public transport commute passengers with different categories and further to better meet their customized travel demands, it is necessary to find ways to identify public transport commuters’ stability accurately. Based on revealed preference survey data, public transport smart card transaction and network data, the travel chain of public transport commuter was obtained by data processing and matching. According to public transport travel behavior data of one month, five characteristic indexes were extracted to represent travel stability from the aspects of activity point, travel space and travel time. These feature indexes include the number of non-home activity categories, the proportion of typical travel chain, space balance degree, time stability and time concentration degree. Through applying FP-growth algorithm, the association rules among each characteristics attributes for different item-sets were mined depending on parameters of support, confidence and lift degree. Then, three categories of public transport commute passengers with apparently different stabilities were identified. Finally, the rationality of the proposed identification method was verified. The study results contribute to developing customized and targeted supply and demand management strategy for public transport travel, which would further help to improve the efficient and delicacy service level of public transport.

Key words: engineering of communications and transportation system, commute passenger, stability identification, association rule

中图分类号:

U491.1

梁泉,翁剑成,周伟,荣建. 基于关联规则的公共交通通勤稳定性人群辨识[J]. 吉林大学学报(工学版), 2019, 49(5): 1484-1491.

Quan LIANG,Jian-cheng WENG,Wei ZHOU,Jian RONG. Stability identification of public transport commute passengers based on association rules[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1484-1491.

图/表 10

表1

个体乘客出行链示意"

卡号	出行模式	上车时间	下车时间	上车线路号	下车线路号	出行距离/m	上车站点	下车站点	上车站点经度/(°)	上车站点纬度/(°)	下车站点经度/(°)	下车站点纬度/(°)
24050273	地铁	2017/4/1 08:28	2017/4/1 08:55	4	1	8115	北京南站	木樨地	116.3779	39.8641	116.3369	39.9075
24050273	地铁	2017/4/1 17:53	2017/4/1 16:29	1	4	8115	木樨地	北京南站	116.3369	39.9075	116.3779	39.8641
$?$
24050273	公交	2017/4/30 17:04	2017/4/30 17:39	114	114	8620	白云桥西	开阳桥南	116.3395	39.8973	116.3471	39.8666

表1

表2

图1

表3

二项集关联规则挖掘表"

关联规则	支持度	置信度	提升度
N3 $?$ A3	0.32	0.84	2.16
A3 $?$ N3	0.32	0.82	2.16
N2 $?$ A2	0.37	0.71	1.55
A2 $?$ N2	0.37	0.80	1.55
R2 $?$ P1	0.35	0.65	1.58
P1 $?$ R2	0.35	0.85	1.58

表3

图2

表4

三项集关联规则挖掘"

关联规则	支持度	置信度	提升度
N3 ∩ A3 $?$ P1	0.24 0.24 0.24	0.75	1.83
A3 ∩ P1 $?$ N3		0.89	2.34
N3 ∩ P1 $?$ A3		1.00	2.56
$?$

表4

表5

四项集关联规则结果表"

关联规则	支持度	置信度	提升度
N3 ∩ A3 ∩ P1 $?$ R2	0.2 0.2 0.2 0.2	0.83	1.54
R2 ∩ A3 ∩ P1 $?$ N3		0.91	2.39
N3 ∩ R2 ∩ A3 $?$ P1		0.83	2.03
N3 ∩ R2 ∩ P1 $?$ A3		1.00	2.56
$?$

表5

表6

图3

图4

参考文献 15

1	北京交通发展研究. 2016年北京市交通发展年度报告[R]. 北京: 北京交通发展研究院, 2016.
2	翁剑成, 王昌, 王月玥, 等. 基于个体出行数据的公共交通出行链提取方法[J]. 交通运输系统工程与信息, 2017, 17(3): 67-73.
	WengJian-cheng, WangChang, WangYue-yue, et al. Extraction method of public transit trip chains based on the individual riders' data[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(3): 67-73.
3	孙世超, 杨东援. 基于朴素贝叶斯分类器的公交通勤人群辨识方法[J]. 交通运输系统工程与信息, 2015, 15(6): 46-53.
	SunShi-chao, YangDong-yuan. Identification of transit commuters based on naïve bayesian classifier[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6): 46-53.
4	袁荣亮. 公共交通通勤者出行方式选择行为研究[D]. 北京: 北京工业大学城市交通学院, 2016.
	YuanRong-liang. Research on travel mode choice behavior of public transport commuters[D]. Beijing: College of Metropolitan Transportation, Beijing University of Technology, 2016.
5	刘花, 陈艳艳. 北京市居民出行方式人群分类[J]. 交通标准化, 2014, 42(5): 6-11.
	LiuHua, ChenYan-yan. Segmentation of travel mode group for Beijing residents[J]. Transportation Standardization, 2014, 42(5): 6-11.
6	狄迪, 杨东援. 基于人群分类的城市公交走廊客流分配模型[J]. 同济大学学报: 自然科学版, 2016, 44(2): 235-242.
	DiDi, YangDong-yuan. A passenger-classification transportation assignment model for urban public traffic corridor[J]. Journal of Tongji University (Natural Science), 2016, 44(2): 235-242.
7	BartD, TijsN, MarioV, et al. Examining commuting patterns using floating car data and circular statistics: exploring the use of new methods and visualizations to study travel times[J]. Journal of Transport Geography, 2015, 48: 41-51.
8	HanJ W, KamberM. Data mining: concepts and techniques[M]. San Francisco: Morgan Kaufmann, 2001.
9	王月玥. 基于多源数据的公共交通通勤出行特征提取方法研究[D]. 北京: 北京工业大学城市交通学院, 2014.
	WangYue-yue. Research on methods of extraction commuting trip characteristic based on public transportation multi-source data[D]. Beijing: College of Metropolitan Transportation, Beijing University of Technology, 2014.
10	AgrawalR, ImielinskiT, SwamiA. Database mining: a performance perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 1993, 5(6): 914-925.
11	AgrawalR, ImielinskiT, SwamiA. Mining association rules between sets of items in large databases[J]. ACM SIGMOD Record, 1993, 22(2): 207-216.
12	崔研, 包志强. 关联规则挖掘综述[J]. 计算机应用研究， 2016, 33(2): 330-334.
	CuiYan, BaoZhi-qiang. Survey of association rule mining[J]. Application Research of Computers， 2016, 33(2): 330-334.
13	FooteP J, StuartD G, Elmore-YalchR. Exploring customer loyalty as a transit performance measure[J]. Transportation Research Record: Journal of the Transportation Research Record, 2001(1753): 93-101.
14	曲昭伟, 姚荣涵, 王殿海. 基于最大信息熵原理的居民出行分布模型[J]. 吉林大学学报: 工学版, 2003, 33(2): 15-19.
	QuZhao-wei, YaoRong-han, WangDian-hai. Inhabits trip distribution model based on maximum information entropy theory[J]. Journal of Jilin University (Engineering and Technology Edition)， 2003, 33(2): 15-19.
15	王秀枝, 安建成. 基于支持度和置信度智能优化的关联分类算法[J]. 计算机应用与软件, 2013, 30(11): 184-186, 198.
	WangXiu-zhi, AnJian-cheng. Associative classification algorithm based on intelligent optimisation of support and confidence[J]. Computer Applications and Software, 2013, 30(11): 184-186, 198.

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稳定性指标	分裂点（C）	分类比例/%	标签	分类属性
N	C12=7 C23=3	10	N1	多
		52	N2	中
		38	N3	少
R	C12=0.59 C23=0.41	7	R1	高
		54	R2	中
		39	R3	低
A	C12=1.46 C23=2.29	15	A1	高
		46	A2	中
		39	A3	低
P	C12=0.82 C23=0.60	41	P1	高
		31	P2	中
		28	P3	低
T	C12=0.46 C23=0.34	27	T1	高
		38	T2	中
		35	T3	低