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

Abstract

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

CLC Number:

U491.1

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.

Figures/Tables 10

Table 1

Travel chain data sample of individual passenger"

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

Table 1

Table 2

Fig.1

Table 3

Mining results of two item-sets association rules"

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

Table 3

Fig.2

Table 4

Mining results of three item-sets"

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
$?$

Table 4

Table 5

Mining results of four item-sets association rules"

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
$?$

Table 5

Table 6

Fig. 3

Fig. 4

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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

序号	关联规则	支持度	分类描述					稳定性
序号	关联规则	支持度	非家活动点类别	典型出行链占比	活动复杂度	时间稳定性	时间集中度	稳定性
1	N3 R2 A3 P1 T1	0.13	少	中	小	高	高	高高
2	N3 R2 A3 P1 T2	0.06	少	中	小	高	中	高高
3	N2 R2 A2 P1 T2	0.09	中	中	中	高	中	中中
4	N2 R2 A2 P2 T2	0.07	中	中	中	中	中	中中
5	N2 R3 A2 P3 T3	0.05	中	少	中	低	低	低低低
6	N2 R3 A1 P2 T3	0.05	中	少	高	中	低
7	N2 R3 A2 P2 T3	0.05	中	少	中	中	低

Stability identification of public transport commute passengers based on association rules

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