Method for Workplace and Residence Identification and Travel Activity Classification Driven by Trajectory Data

doi:10.12141/j.issn.1000-565X.240196

Abstract

Abstract:

Vehicle trajectory data contains rich information about travel behavior. By analyzing the origins and destinations within these trajectories-such as places of residence, work, and travel-related points of interest (POIs)-researchers can deeply examine travel activity characteristics and patterns. This study identified potential residential locations based on the first origin and the final destination of each day’s travel activities, while other origins and destinations form the candidate sets for workplaces and interest points. Building on these candidate sets, the paper proposed a method for identifying places of residence and employment using mean shift clustering with spatiotemporal constraints. The identification is based on three criteria: cluster density, average dwell time within the cluster, and the time range of travel activities. This approach enables the extraction of residential, work, and interest point coordinates. The KD-Tree algorithm was then used to match each identified coordinate with nearby POIs, providing specific names and locations for residences and workplaces. Based on the identification of workplace and travel interests, the study quantified travel activity levels using metrics such as travel frequency, distance, and time. It also characterized travel regularity using stability and variability indicators. A K-means++ clustering algorithm was employed to classify types of travel activity patterns. Taking 1 708 vehicles with travel activities for 34 days in Beijing as an example, the empirical research was conducted based on the driving trajectory data. The research results indicate that the distribution characteristics of workplace and residential areas determined by the proposed method is consistent with practical laws, with high accuracy and reliability. The classification of travel activity characteristics based on the K-means++ algorithm reveals that active travel patterns dominate in megacities, accounting for 59.84% of total activities. Among these, frequent travelers constitute the largest subgroup at 31.09%. Active travel behaviors primarily manifest as regular trips on weekdays and irregular trips on non-working days. This research provides theoretical support for optimizing transportation infrastructure planning.

Key words: urban transportation, workplace and residence, travel activities, trajectory data, clustering algorithm

CLC Number:

U491.1

XIE Kun, XING Xinyuan, DONG Honghui, DONG Chunjiao, CHEN Yuanduo. Method for Workplace and Residence Identification and Travel Activity Classification Driven by Trajectory Data[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(6): 131-139.

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用地类型代码	居住地	工作地
5179094	太玉园东区41号楼	北京长空机械有限责任公司办公楼
5190473	沙子口路甲48号院 3号楼	体育馆路街道东玉北街社区文化广场
5828175	遂心苑小区	奥迪销售中心（亚市专营店）
5828403	安达旅店（小红门路）	北京泰德制药有限公司停车场
6833858	南十里居104号楼	故宫陶瓷馆（暂停营业）
7026942	万象公馆	北京市三帆中学
7176077	南岗洼158公寓	北京门头沟供电公司
7759746	草桥欣园4区17号楼	北京万峰石材
7942794	黄寺大街24号院 3号楼	慧怡企业园
8060111	豆瓣公寓	吉易建成（北京）建筑装饰工程有限公司

出行分类	平均出行时间/min	平均出行距离/km	出行距离与时间比/（km∙min^-1）
工作日通勤出行	57.498 1	12.026 3	0.209 2
工作日非通勤出行	67.937 2	19.010 2	0.279 8
休息日出行	62.881 5	17.687 7	0.281 3

类别	出行时间/ min	出行距离/ km	出行次数	平均出行时间/min	平均出行距离/km
均值	64.05	16.35	3.58	17.90	4.57
1	55.09	19.77	5.58	9.88	3.55
2	39.95	8.29	2.38	16.81	3.49
3	83.30	40.68	3.28	25.41	12.41
4	124.71	11.71	2.97	41.93	3.94

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