融合出行拓扑与序列分析的车辆时空出行模式挖掘

doi:10.16097/j.cnki.1009-6744.2023.02.005

交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (2): 40-53.DOI: 10.16097/j.cnki.1009-6744.2023.02.005

• 智能交通系统与信息技术 • 上一篇下一篇

融合出行拓扑与序列分析的车辆时空出行模式挖掘

金盛^*1a,1b，苏弘扬^1a,1c，张静²

1. 浙江大学，a. 智能交通研究所，b. 平衡建筑研究中心，c. 工程师学院，杭州 310058； 2. 浙江公路水运工程咨询有限责任公司，杭州 310000

收稿日期:2022-12-30 修回日期:2023-02-01 接受日期:2023-02-14 出版日期:2023-04-25 发布日期:2023-04-19
作者简介:金盛(1982- )，男，浙江温州人，教授，博士
基金资助:
浙江省自然科学基金杰出青年基金(LR23E080002)；浙江省交通运输厅科技计划项目 (202212)；国家自然科学基金 (92046011)。

Inferring Spatial-temporal Travel Patterns of Vehicles Combining Topology of Trips and Sequence Analysis

JIN Sheng^*1a,1b, SU Hong-yang^1a,1c , ZHANG Jing²

1a. Institute of Intelligent Transportation Systems, 1b. Center for Balance Architecture, 1c. Polytechnic Institute, Zhejiang University, Hangzhou 310058, China; 2. Zhejiang Highway & Water Transportation Engineering Co. Ltd., Hangzhou 310000, China

Received:2022-12-30 Revised:2023-02-01 Accepted:2023-02-14 Online:2023-04-25 Published:2023-04-19
Supported by:
Natural Science Fund for Distinguished Young Scholars of Zhejiang Province, China (LR23E080002)；Science and Technology Program of Zhejiang Provincial Department of Transport (202212)；National Natural Science Foundation of China (92046011)

摘要/Abstract

摘要： 城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性，本文基于车牌识别数据，全量感知车辆出行活动，分析城市中个体车辆的出行时空模式。首先，从数据中提取并分离车辆出行链，获得小汽车出行的时间、空间、频率和拓扑特征，根据各时段停留点构造车辆出行活动序列。其次，融合兴趣点 (Point of Interest, POI)数据识别出行起讫点关联的土地利用特性作为停留点特征，在出行活动序列上应用k-modes聚类算法挖掘出常规通勤模式、特殊通勤模式、短时活动模式和外来办事模式这4类30种小汽车出行模式。最后，对每一类模式的群体规模、特征和典型出行行为进行详细地分析讨论。结果表明，95%的车辆出行活动可以用不多于3条边组成的简单拓扑结构表示，其中，约30%的车辆可构造出行活动序列，并用k-modes聚类算法有效分离出各类机动车全天出行的时空模式。工作日车辆出行主要表现为常规通勤模式，休息日则以短时活动模式为主。通过对个体车辆的微观行为分析，结合出行拓扑结构和出行活动序列进行出行模式的挖掘，能够全面地反映城市机动车出行的实际情况，为精细化机动车出行行为分析与管控策略制定提供理论支撑。

关键词: 城市交通, 时空出行模式, 聚类分析, 车牌识别数据, 车辆出行行为

Abstract: The analysis of spatial-temporal characteristics of vehicle travel is the basis of urban traffic planning, design, and traffic demand management. License Plate Recognition (LPR) data can detect every vehicle in a road network, breaking the limitations of aggregated or sampled data used in traditional travel behavior research. In this paper, LPR data was employed to infer spatial-temporal travel patterns of individual vehicles. Firstly, trip chains were extracted from LPR data and then cut off to obtain vehicle trips with time, location, frequency, and topology features. Travelactivity sequences were constructed based on the locations where vehicles stay during the day. Point of Interest (POI) data was used to identify the land use characteristics associated with trip origin- destination that were regarded as features of staying location. A total of 30 spatial-temporal vehicle travel patterns were mined using the k-modes algorithm. It is found that all the travel patterns were divided into four categories: regular commuting pattern, special commuting pattern, short-term activity pattern, and non-native travel pattern. The population, features, and typical behaviors of each pattern are discussed. The results show that 95% of vehicle travel activities can be represented by a simple topology structure consisting of less than 3 edges, 30% of which can be used for constructing activity sequences. The k-modes clustering algorithm is capable to distinguish various spatial-temporal travel patterns of vehicles based on activity sequence. Regular commuting pattern dominates vehicle travels on weekdays, while shortterm activity pattern is the majority on weekends. With the analysis of microscopic behaviors, the combination of topological structure and activity sequence benefits the mining of individual vehicle travel patterns. This paper provides theoretical support for detailed vehicle travel behavior analysis as well as traffic management and control strategy for decision-makers.

Key words: urban traffic, spatial-temporal travel pattern, clustering analysis, license plate recognition data, vehicle travel behavior

中图分类号:

U491.2

金盛, 苏弘扬, 张静. 融合出行拓扑与序列分析的车辆时空出行模式挖掘[J]. 交通运输系统工程与信息, 2023, 23(2): 40-53.

JIN Sheng, SU Hong-yang, ZHANG Jing. Inferring Spatial-temporal Travel Patterns of Vehicles Combining Topology of Trips and Sequence Analysis[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(2): 40-53.

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融合出行拓扑与序列分析的车辆时空出行模式挖掘

Inferring Spatial-temporal Travel Patterns of Vehicles Combining Topology of Trips and Sequence Analysis

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