基于时空聚类算法的轨迹停驻点识别研究

摘要/Abstract

摘要：

利用手机等智能移动终端获取个体出行GPS轨迹数据，提出了一种新的时空聚类算法AT-DBSCAN识别轨迹中的停驻点.该方法以固定长度的滑窗搜索核心点，以时空邻近条件定义簇间距离，以簇密度大小规定合并次序.提出了出行次数一致性、出行起止时刻误差、停驻点时长误差、停驻点中心偏移距离4个算法验证指标，弥补了传统查全率、查准率等忽略停驻点时空信息准确性的不足.结果表明，识别的停驻点有98%的位置误差在30m以内，100%的时长误差在5min以内，98%的出行起止时刻误差在5min以内.此外，算法对于室内活动、定位飘移、路径重合程度高等复杂轨迹具有较好的泛化能力.

关键词: 信息技术, 停驻点识别, 时空聚类算法, 个体出行调查, GPS

Abstract:

Based on the individual GPS data collected by the mobile terminal like smartphones, a novel spatiotemporal clustering algorithm called AT-DBSCAN is proposed to identify anchors in the trajectories. In this algorithm, the core points are found and checked within a sliding window, the similarity is defined by the temporospatial difference, and clusters are merged according to the density. Rather than using the recall or precision to evaluate results, the consistency of travel times, the comprehensive bias when the travel starts and ends, the difference between anchors duration, and the central distance between the true and identified anchors, are proposed and applied to get the exact temporal-spatial information. Results suggest that 98 percent anchors’ distances are less than 30 meters, 100 percent duration difference are less than 5 minutes, and 98 percent travel time difference are within 5 minutes. Besides, the proposed algorithm also has a good performance in generalization of indoor activity, positioning drift and severe overlying segments.

Key words: information technology, anchor identification, spatiotemporal clustering algorithm, individual travel survey, GPS

中图分类号:

U491

周洋，杨超. 基于时空聚类算法的轨迹停驻点识别研究[J]. 交通运输系统工程与信息, 2018, 18(4): 88-95.

ZHOU Yang, YANG Chao. Anchors Identification in Trajectory Based on Temporospatial Clustering Algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(4): 88-95.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract19665.shtml

http://www.tseit.org.cn/CN/Y2018/V18/I4/88

参考文献

[1] COTTRILL C, PEREIRA F, ZHAO F, et al. Future mobility survey: Experience in developing a smartphone- based travel survey in Singapore[J]. Transportation Research Record: Journal of the Transportation Research Board, 2013 (2354): 59-67.

[2] DU J, AULTMAN-HALL L. Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues[J]. Transportation Research Part A: Policy and Practice, 2007, 41(3): 220-232.

[3] 张健钦, 仇培元, 徐志洁, 等. 一种基于手机定位数据的出行行程识别方法[J]. 武汉理工大学学报(交通科学与工程版), 2013, 37(5): 934-938. [ZHANG J Q, QIU P Y, XU Z J, et al. A method to identify trip based on the mobile phone positioning data[J]. Journal of Wuhan University of Technology: Transportation Science & Engineering, 2013, 37(5): 934-938.]

[4] 肖光年, 隽志才, 高晶鑫. 基于 GPS 定位数据的出行端点推断[J]. 吉林大学学报(工学版), 2016, 46(3): 770-776. [XIAO G N, JUAN Z C, GAO J X. Inference method of trip ends based on GPS track data[J]. Journal of Jilin University(Engineering and Technology Edition), 2016, 46(3): 770-776.]

[5] ALVARES L O, BOGORNY V, KUIJPERS B, et al. A model for enriching trajectories with semantic geographical information[C]//Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, ACM, 2007: 22.

[6] PALMA A T, BOGORNY V, KUIJPERS B, et al. A clustering- based approach for discovering interesting places in trajectories[C]//Proceedings of the 2008 ACM Symposium on Applied Computing, ACM, 2008: 863-868.

[7] ESTER M, KRIEGEL H P, SANDER J, et al. A densitybased algorithm for discovering clusters in large spatial databases with noise[C]// Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, 1996: 226-231.

[8] BIRANT D, KUT A. ST-DBSCAN: An algorithm for clustering spatial–temporal data[J]. Data & Knowledge Engineering, 2007, 60(1): 208-221.

[9] TRAN L H, NGUYEN Q V H, DO N H, et al. Robust and hierarchical stop discovery in sparse and diverse trajectories [J/OL]. (2016-08-09)[2018-01-22]. https:// infoscience.epfl.ch/record/175473.

[10] GONG L, SATO H, YAMAMOTO T, et al. Identification of activity stop locations in GPS trajectories by densitybased clustering method combined with support vector machines[J]. Journal of Modern Transportation, 2015, 23 (3): 202-213.

[11] 向隆刚, 邵晓天. 载体轨迹停留信息提取的核密度法及其可视化[J]. 测绘学报, 2016, 45(9): 1122-1131. [XlANG L G, SHAO X T. Visualization and extraction of trajectory stops based on kernel-density[J]. Acta Geodaeticaet Cartographica Sinica, 2016, 45(9): 1122-1131.]

[12] 何源浩, 魏海平, 周烨, 等. 车辆 GPS 轨迹兴趣区域提取算法研究[J]. 测绘工程, 2016, 25(5): 47-51. [HE Y H, WEI H P, ZHOU Y, et al. Algorithm for extracting regions of interest from vehicle GPS trajectory[J]. Engineering of Surveying and Mapping, 2016, 25(5): 47-51.]

[13] XIAO G, JUAN Z, ZHANG C. Travel mode detection based on GPS track data and Bayesian networks[J]. Computers, Environment and Urban Systems, 2015(54): 14-22.

[14] WAGNER D P. Lexington area travel data collection test: GPS for personal travel surveys[R]. Final Report, Office of Highway Policy Information and Office of Technology Applications, Federal Highway Administration, Battelle Transport Division, Columbus, 1997: 1-92.

[15] FAN Y, WOLFSON J, ADOMAVICIUS G, et al. SmarTrAC: A smartphone solution for context-aware travel and activity capturing[R]. Final Report, U.S. Department of Transportation, U.S. Department of Transportation, 2015.

[16] 柴彦威, 申悦, 马修军, 等. 北京居民活动与出行行为时空数据采集与管理[J]. 地理研究, 2013, 32(3): 441-451. [CHAI Y W, SHEN Y, MA X J, et al. The collection and management of space-time data of individual behavior based on location-based technologies: A case study of activity-travel survey in Beijing[J]. Geographical Research, 2013, 32(3): 441-451.]

[17] 杨飞, 姚振兴. 基于手机传感器数据的出行特征提取方法[J]. 城市交通, 2016, 14(1): 9-14. [YANG F, YAO Z X. Cellular-based data extracting method for travel characteristics[J]. Urban Transport of China, 2016, 14 (1): 9-14.]

[18] JI Y, GAO L, CHEN D, et al. Functional analysis of public transport network in trip mode detection from personal smartphone trajectory data[C]//96th Annual Meeting of the Transportation Research Board, 2017.

[19] 杨超, 朱荣荣, 涂然. 基于智能手机调查数据的居民出行活动特征分析[J]. 交通信息与安全, 2015, 33(6): 25-32. [YANG C, ZHU R R, TU R. Analysis of the travel characteristics of residents in Shanghai using the itinerary data collected from smart phones[J]. Journal of Transport Information and Safety, 2015, 33(6): 25-32.]

[20] SCHUESSLER N, AXHAUSEN K. Processing raw data from global positioning systems without additional information[J]. Transportation Research Record: Journal of the Transportation Research Board, 2009 (2105): 28-36.