Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (1): 177-184.DOI: 10.11772/j.issn.1001-9081.2020060937

Special Issue: 第八届中国数据挖掘会议(CCDM 2020)

• China Conference on Data Mining 2020 (CCDM 2020) • Previous Articles     Next Articles

Work location inference method with big data of urban traffic surveillance

CHEN Kai1, YU Yanwei2, ZHAO Jindong1, SONG Peng1   

  1. 1. School of Computer and Control Engineering, Yantai University, Yantai Shandong 264005, China;
    2. Department of Computer Science and Technology, Ocean University of China, Qingdao Shandong 266100, China
  • Received:2020-07-02 Revised:2020-08-31 Online:2021-01-10 Published:2020-09-15
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61773331).


陈凯1, 于彦伟2, 赵金东1, 宋鹏1   

  1. 1. 烟台大学 计算机与控制工程学院, 山东 烟台 264005;
    2. 中国海洋大学 计算机科学与技术系, 山东 青岛 266100
  • 通讯作者: 于彦伟
  • 作者简介:陈凯(1993-),男,山东高密人,硕士,CCF会员,主要研究方向:时空数据挖掘;于彦伟(1986-),男,山东菏泽人,副教授,博士,CCF会员,主要研究方向:时空数据挖掘、机器学习;赵金东(1974-),男,山东滨州人,副教授,博士,CCF会员,主要研究方向:物联网、区块链;宋鹏(1983-),男,山东莱阳人,副教授,博士,CCF会员,主要研究方向:语音信号处理。
  • 基金资助:

Abstract: Inferring work locations for users based on spatiotemporal data is important for real-world applications ranging from product recommendation, precise marketing, transportation scheduling to city planning. However, the problem of location inference based on urban surveillance data has not been explored. Therefore, a work location inference method was proposed for vehicle owners based on the data of traffic surveillance with sparse cameras. First, the urban traffic periphery data such as road networks and Point Of Interests (POIs) were collected, and the preprocessing method of road network matching was used to obtain a real road network with rich semantic information such as POIs and cameras. Second, the important parking areas, which mean the candidate work areas for the vehicles were obtained by clustering Origin-Destination (O-D) pairs extracted from vehicle trajectories. Third, using the constraint of the proposed in/out visiting time pattern, the most likely work area was selected from multiple area candidates. Finally, by using the obtained road network and the distribution of POIs in the road network, the vehicle's reachable POIs were extracted to further narrow the range of work location. The effectiveness of the proposed method was demonstrated by comprehensive experimental evaluations and case studies on a real-world traffic surveillance dataset of a provincial capital city.

Key words: data mining, urban computing, traffic surveillance data, work location inference, Point Of Interest (POI)

摘要: 基于时空数据的用户位置推理在产品推荐、精确营销、交通调度及城市规划等实际应用中有着重要的作用,然而,基于城市交通监控数据的位置推理问题尚未被探索,因此,提出了一种面向稀疏摄像头交通监控数据的工作位置推理方法。首先,收集了路网、兴趣点(POI)等城市交通外围数据,并通过路网匹配的预处理方式获取到了一个含有摄像头、POI等丰富语义信息的真实路网;其次,通过聚类车辆轨迹中所提取的起点-终点(O-D)对来获得车辆重要的停留区域,即候选工作区域;之后,利用所提的in/out访问时间模式的约束,从多个候选区域中匹配出最大可能的工作区域;最后,利用所获取的路网信息和路网周中POI的分布信息提取出车辆的可达POI集合,从而进一步缩小车主的工作位置范围。在一个省会城市真实的交通监控数据集上的综合实验评估和案例分析验证了所提方法的有效性。

关键词: 数据挖掘, 城市计算, 交通监控数据, 工作位置推理, 兴趣点

CLC Number: