Research of Track Resident Point Identification Algorithm Based on Signaling Data

Wanlin LI; Chao WANG; Guoliang XU; Jiangtao LUO; Xuan ZHANG

doi:10.11999/JEIT190914

Volume 42 Issue 12

Dec. 2020

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Wanlin LI, Chao WANG, Guoliang XU, Jiangtao LUO, Xuan ZHANG. Research of Track Resident Point Identification Algorithm Based on Signaling Data[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3013-3020. doi: 10.11999/JEIT190914

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Wanlin LI, Chao WANG, Guoliang XU, Jiangtao LUO, Xuan ZHANG. Research of Track Resident Point Identification Algorithm Based on Signaling Data[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3013-3020. doi: 10.11999/JEIT190914

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Research of Track Resident Point Identification Algorithm Based on Signaling Data

doi: 10.11999/JEIT190914

Wanlin LI¹,
Chao WANG^{1, 2},
Guoliang XU^{2
,
,},
Jiangtao LUO²,
Xuan ZHANG^{1, 2}

1.
Institute of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Electronic Information and Networking Research Institute, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The Natural Science Foundation of Chongqing (cstc2018jcyjAX0587), The New Sensing Technology, Information Fusion Processing and its Application (A2017-10)

Received Date: 2019-11-14
Rev Recd Date: 2020-06-09

Available Online: 2020-07-16

Publish Date: 2020-12-08

Abstract

Abstract

For the problem that the density-based clustering algorithm can only identify clusters with similar density and high computational complexity, a Clustering by Fast Search and Find of Density Peaks based on Spatio-Temporal trajectory information in mobile phone signaling data, namely ST-CFSFDP, is proposed. Firstly, the low sampling density signaling data are pre-processed to eliminate the trajectory oscillation phenomenon in the data. Then, based on the Clustering by Fast Search and Find of Density Peaks(CFSFDP) algorithm, the time dimension limitation is explicitly increased, and the local density is extended from two-dimension to three-dimension. Moreover, in order to characterize the cluster center point in the time dimension, the concept of high-density time interval is defined. Secondly, the suitable cluster center screening strategy is developed to select automatically the appropriate cluster center. Finally, the resident points are identified in the travel trajectory of individual users over a period of time and the division of the travel chains is completed. The experimental results show that the algorithm is suitable for signaling data with low sampling density and poor positioning accuracy. It is more suitable for spatio-temporal data than CFSFDP algorithm. Compared with Density-Based Spatial Clustering of Applications with Noise based on Spatio-Temporal data (ST-DBSCAN) algorithm, the recall rate is improved by 14%, the accuracy rate is increased by 8%, and the computational complexity is also reduced.
- Signaling data,
- Spatio-temporal clustering,
- Clustering by Fast Search and Find of Density Peaks (CFSFDP),
- Residual point recognition,
- Travel chain

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