基于轨迹数据的大规模路网交通拥挤时空关联规则挖掘

doi:10.16182/j.issn1004731x.joss.22-0898

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (1): 260-271.doi: 10.16182/j.issn1004731x.joss.22-0898

• 论文 • 上一篇

基于轨迹数据的大规模路网交通拥挤时空关联规则挖掘

周启帆¹(), 刘海旭¹^,²^,³^,⁴(), 董志鹏¹, 徐银¹

^1.西南交通大学交通运输与物流学院，四川成都 611756
^2.西南交通大学综合交通大数据应用技术国家工程实验室，四川成都 611756
^3.西南交通大学综合交通运输智能化国家地方联合工程实验室，四川成都 610031
^4.西南交通大学综合运输四川省重点实验室，四川成都 611756

收稿日期:2022-08-03 修回日期:2022-11-06 出版日期:2024-01-20 发布日期:2024-01-19
通讯作者: 刘海旭 E-mail:2015114658@my.swjtu.edu.cn;hxliu@swjtu.edu.cn
第一作者简介:周启帆(1996-)，男，硕士生，研究方向为交通建模与仿真。E-mail：2015114658@my.swjtu.edu.cn
基金资助:
国家自然科学基金(61873126);湖北省交通运输厅科技项目(2022-11-1-5)

Spatio-temporal Association Rule Mining of Traffic Congestion in a Large-scale Road Network Based on Trajectory Data

Zhou Qifan¹(), Liu Haixu¹^,²^,³^,⁴(), Dong Zhipeng¹, Xu Yin¹

^1.School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
^2.National Engineering Laboratory of Application Technology of Integrated Transportation Big Data, Southwest Jiaotong University, Chengdu 611756, China
^3.National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, China
^4.Comprehensive Transportation Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, China

Received:2022-08-03 Revised:2022-11-06 Online:2024-01-20 Published:2024-01-19
Contact: Liu Haixu E-mail:2015114658@my.swjtu.edu.cn;hxliu@swjtu.edu.cn

摘要/Abstract

摘要：

提出了K近邻RElim（K neighbor-RElim，KNR）算法和时序K近邻RElim（sequential KNbr-RElim，SKNR）算法，利用大规模路网的车辆轨迹数据来挖掘路段拥挤关联规则和拥挤传播时空关联规则。其中KNR算法在RElim算法基础上拓展了空间拓扑约束，可高效从大规模车辆轨迹数据集中挖掘路网中关联性拥挤易发路段，并量化这些路段间拥挤的关联性强度。而SKNR算法进一步以滑动窗口的形式拓展时间维度，可以挖掘出大规模路网中难以直接观测的拥挤传播现象，并追溯拥挤传播路径。以成都路网和车辆轨迹数据的挖掘结果对所提出的算法进行了说明和验证，结果表明了算法的有效性和鲁棒性。

关键词: 数据挖掘, 关联规则, 拥挤传播, 轨迹数据, RElim算法

Abstract:

A K neighbor-RElim (KNR) algorithm and a sequential KNbr-RElim (SKNR) algorithm are proposed to mine traffic congestion association rules and congestion propagation spatio-temporal association rules by vehicle trajectory data in a large-scale road network. The KNR algorithm extends the spatial topology constraint based on the RElim algorithm. The KNR can be used to mine the road links prone to congestion from the large-scale trajectory dataset in a large-scale road network and quantify the strength of association for congested road links. The SKNR algorithm expands the time dimension in the form of sliding window and can be applied for mining the congestion propagation phenomenon which is difficult to observe directly in a large-scale road network and tracing the path of congestion propagation. The algorithms are illustrated and verified by the empirical results of the Chengdu road network with vehicle trajectory data. The results show the effectiveness and robustness of the proposed algorithms.

Key words: data mining, association rules, congestion propagation, trajectory data, RElim algorithm

中图分类号:

TP301.6

周启帆,刘海旭,董志鹏等 . 基于轨迹数据的大规模路网交通拥挤时空关联规则挖掘[J]. 系统仿真学报, 2024, 36(1): 260-271.

Zhou Qifan,Liu Haixu,Dong Zhipeng,et al . Spatio-temporal Association Rule Mining of Traffic Congestion in a Large-scale Road Network Based on Trajectory Data[J]. Journal of System Simulation, 2024, 36(1): 260-271.

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ID	AB	conf_A	KULC	IR
1	(4548211352, 5530491877, 0), (7366295632, 4548211352, 0)	0.94	0.76	1.66
2	(671600440, 6224263690, 0), (6224263690, 6224263706, 0)	0.76	0.70	1.19
3	(5530491877, 4548211421, 0), (4548211352, 5530491877, 0), (7366295632, 4548211352, 0)	0.99	0.70	2.47
4	(963638438, 5535297029, 0), (5535297029, 1159223970, 0)	0.74	0.62	1.48
5	(963638438, 5535318948, 0), (5535318948, 5218240010, 0)	0.76	0.68	1.27
6	(1339701401,963638438,0), (4549993385,1339701401, 0)	0.73	0.71	1.07

ID	AB	conf_A	KULC	IR
1	(7366295632, 4548211352, 0, 1),(4548211352, 5530491877, 0, 2)	0.74	0.59	1.66
2	(7366295632, 4548211352, 0, 1), (5530491877, 4548211421, 0, 1), (4548211352, 5530491877, 0, 2)	0.76	0.53	2.47
3	(5530491877, 4548211421, 0, 1), (4548211352, 5530491877, 0, 2), (4548211352, 5530491877, 0, 3)	0.71	0.56	1.76
4	(7366295632, 4548211352, 0, 1), (4548211352, 5530491877, 0, 2), (4548211352, 5530491877, 0, 3)	0.73	0.53	2.26
5	(314622918, 672287905, 0, 1), (1159945489, 4548211258, 0， 1), (314622918, 672287905, 0, 2)	0.71	0.57	1.61
6	(1159945489, 4548211258, 0, 1), (314622918, 672287905, 0, 2), (314622918, 672287905, 0, 3)	0.72	0.59	1.61

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基于轨迹数据的大规模路网交通拥挤时空关联规则挖掘

Spatio-temporal Association Rule Mining of Traffic Congestion in a Large-scale Road Network Based on Trajectory Data

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