为研究智能手机所采集到的位置数据在识别用户出行方式领域的应用，首先，比选出速度、速度的百分位数、轨迹点数量占比、出行距离、停止率这5 个适用于移动终端定位数据区分出行方式的特征变量，并对各特征变量的判别阈值进行了定义。然后，针对基站分布导致的数据偏差和位置信息漂移等问题，采用扇形定位结合地图匹配技术对数据进行了修正，进而在对时间阈值和距离阈值分割的基础上提出了移动终端用户出行链的获取方法。接着，建立C4.5 决策树模型，以此判别移动终端用户的出行方式。最后，基于在某地区采集的7 000 部移动终端的位置数据（包含：终端编号、定位时刻、经度、纬度） 来对用户的出行方式进行研究。结果表明，模型在区分机动车和非机动车时准确率较高，达到了90%以上；在进一步区分中，如区分步行与自行车以及公交车和小汽车的出行上准确率相对较低，但也达到了80%以上的精度。
Abstract
In order to study the application of location data of intelligent mobile phones in the field of identifying the users’travel modes, firstly, five characteristic variables applied to distinguish travel modes by mobile terminal positioning data were compared and selected, including speed, percentile of speed, proportion of track point number, trip distance and stopping rate. The threshold value of each characteristic variable was determined. Then, aiming at the problems of data deviation and position information drift and other problems caused by base station distribution, the data was modified by sector positioning and map matching technology. Based on the segmentation of time threshold and distance threshold, the acquisition method of mobile terminal user travel chain was proposed. After that, C4.5 decision tree model was established to distinguish mobile terminal users’travel modes. Finally, the location data, including terminal number, positioning time, longitude and latitude, of 7 000 mobile terminals in one area was collected to study those users’travel modes. The results showed that there was high accuracy in distinguishing motor vehicles and non-motor vehicles, which was more than 90%. Then in further distinguish, such as between walking and cycling, as well as bus and car travel, there was relatively low accuracy, but it also achieved an accuracy of more than 80%.
关键词
移动终端 /
位置数据 /
出行方式 /
城市规划 /
决策树
Key words
mobile terminal /
location data /
travel mode /
urban planning /
decision tree
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] LIN M, HSU W J. Mining GPS Data for Mobility Patterns: A Survey[J]. Pervasive and Mobile Computing, 2014, 12: 1-16.
[2] 谢幸，郑宇. 基于地理信息的用户行为理解[J]. 计算机学会通讯，2008，4（10）：45-51.
[3] CHANG C C, LIN C J. LIBSVM: a Library for Support Vector Machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27.
[4] GONG H M, CHEN C, BIALOSTOZKY E, et al. A GPS/GIS Method for Travel Mode Detection in New York City[J]. Computers, Environment and Urban Systems, 2012, 36(2): 131-139.
[5] LU J G, DI S, PAN C X, et al. Applying Cellular-Based Location Data to Urban Transportation Planning[C]// Proceedings of Ninth International Conference on Applications of Advanced Technology in Transportation. Chicago: AATT, 2006: 713-718.
[6] STOPHER P, FITZGERALD C, ZHANG J. Search for a Global Positioning System Device to Measure Person Travel[J]. Transportation Research Part C: Emerging Technologies, 2008, 16(3): 350-369.
[7] BOHTE W, MAAT K. Deriving and Validating Trip Purposes and Travel Modes for Multi-Day GPS-Based Travel Surveys: A Large-Scale Application in the Netherlands[J]. Transportation Research Part C, 2009, 17(3): 285-297.
[8] CHEN C, GONG H M, LAWSON C, et al. Evaluating the Feasibility of a Passive Travel Survey Collection in a Complex Urban Environment: Lessons Learned from the New York City Case Study[J]. Transportation Research Part A: Policy and Practice, 2010, 44(10): 830-840.
[9] 闫彭. 基于AGPS 手机的交通方式识别研究[D]. 北京：北京交通大学，2012.
[10] 张治华. 基于GPS轨迹的出行信息提取研究[D]. 上海：华东师范大学，2010.
[11] 汪磊， 左忠义， 傅军豪. 基于SVM的出行方式特征分析和识别研究[J]. 交通运输系统工程与信息， 2014，14（3）：70-75，84.
[12] 杨彪. 基于智能手机GPS 的大学生出行方式识别研究[D]. 南京：江苏大学，2017.
[13] 李喆，孙健，倪训友. 基于智能手机大数据的交通出行方式识别研究[J]. 计算机应用研究，2016，33（12）：3527-3529，3558.
[14] 李娅. 基于智能手机的交通方式识别的研究与设计[D]. 湘潭：湖南科技大学，2016.
[15] 肖艳丽，张振宇，杨文忠. 移动数据的交通出行方式识别方法[J]. 智能系统学报，2014，9（5）：536-543.
[16] DEVILLE P, LINARD C, MARTIN S, et al. Dynamic Population Mapping Using Mobile Phone Data[J]. Proceedings of the National Academy of Sciences, 2014, 111(45): 15888-15893.
[17] 冉斌. 手机数据在交通调查和交通规划中的应用[J]. 城市交通，2013，11（1）：72-81.
[18] 赖见辉. 基于移动通信定位数据的交通信息提取及分析方法研究[D]. 北京：北京工业大学，2014.
基金
国家自然科学基金项目（61872037）；国家自然科学基金项目（61833002）；深圳市交通公用设施建设项目（BYTD-KT-002-2）
PDF(1861 KB)
京公网安备 11010102003417号
京ICP备17006742号-3
