Journal of Geo-information Science, Volume. 22, Issue 1, 136(2020)
Figures & Tables(11)
Fig. 1. Basic procedures of the individual stay behavior prediction method
Fig. 3. Number of location update records for mobile users from Aug.10, 2015 to Aug.29, 2015
Fig. 4. The prediction accuracy distribution of mobile phone user stay behavior
Fig. 5. Comparison of mobile phone user stay behavior prediction results with different feature sets
Fig. 6. The chi-square value of the top20 stay behavior prediction features
Table 1. The examples of one user′s mobile phone location update data records
View tableView in ArticleTable 1. The examples of one user′s mobile phone location update data records
用户ID 日期 时间 事件类型 基站编号 基站经度/° 基站纬度/° 58**** 20YY-MM-DD 07:30 1 11** 115.**** 29.**** 58**** 20YY-MM-DD 08:30 5 11** 115.**** 29.**** … … … 3 … … … 58**** 20YY-MM-DD 21:30 4 10** 115.**** 29.**** 58**** 20YY-MM-DD 22:30 10 11** 115.**** 29.****
Table 2. The examples of one user′s Internet traffic data records
View tableView in ArticleTable 2. The examples of one user′s Internet traffic data records
用户ID 归属地 日期 时间 基站编号 经度/° 纬度/° APP 流量/G 58*** HB.WH 20YY-MM-DD 07:32 11** 115.*** 29.*** 高德地图 0.0016 58*** \N 20YY-MM-DD 08:32 11** 115.*** 29.*** 微信 0.0092 58*** \N 20YY-MM-DD 09:27 12** 115.*** 29.*** QQ 0.0242 … … … … … … … … … 58*** \N 20YY-MM-DD 21:06 10** 115.*** 29.*** 微信 0.0016 58*** \N 20YY-MM-DD 21:34 11** 115.*** 29.** 新浪新闻 0.0554
Table 3. The statistics of mobile phone user stay behavior from Aug.10, 2015 to Aug.29, 2015
View tableView in ArticleTable 3. The statistics of mobile phone user stay behavior from Aug.10, 2015 to Aug.29, 2015
时段 移动/% 停留/% 无法识别/% 6 14.51 52.41 33.08 7 24.93 49.99 25.07 8 31.66 49.87 18.46 9 33.00 49.57 17.43 10 32.60 49.77 17.63 11 31.79 49.81 18.40 12 30.75 50.16 19.09 13 27.16 51.36 21.48 14 28.41 51.06 20.54 15 28.23 50.96 20.81 16 28.33 50.30 21.37 17 29.59 49.10 21.31 18 30.97 47.30 21.73 19 29.37 50.16 20.47 20 27.33 51.46 21.20 21 22.68 52.60 24.72
Table 4. The recognition rate of mobile phone users' home and work location from Aug.10, 2015 to Aug.29, 2015
View tableView in ArticleTable 4. The recognition rate of mobile phone users' home and work location from Aug.10, 2015 to Aug.29, 2015
地点 识别率/% 家 78.53 工作地 47.06 同时识别率 40.09
Table 5. Comparison of mobile phone user stay behavior prediction effects between different prediction algorithms
View tableView in ArticleTable 5. Comparison of mobile phone user stay behavior prediction effects between different prediction algorithms
预测模型 准确率/% 运行时间/s 参数设置 本文模型 80.31 0.141 SVM 77.35 3.996 kernel='linear' probability=True GBDT 76.13 1.940 n_estimators=200 RF 81.54 0.960 n_estimators=200 一阶Markov 71.75 - MostValue 69.45 -
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Zhixiang FANG, Yaqian NI, Shouqian HUANG.
Paper Information
Received: Nov. 4, 2019
Accepted: --
Published Online: Sep. 16, 2020
The Author Email: FANG Zhixiang (zxfang@whu.edu.cn)
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