Evaluation of Activity Location Recognition Using Cellular Signaling Data

YANG Fei; JIANG Haihang; YAO Zhenxing; LIU Haode

doi:10.3969/j.issn.0258-2724.20200086

Volume 56 Issue 5

Oct. 2021

Turn off MathJax

Article Contents

Abstract

References

Journal of Southwest Jiaotong University > 2021 > 56(5): 928-936.

YANG Fei, JIANG Haihang, YAO Zhenxing, LIU Haode. Evaluation of Activity Location Recognition Using Cellular Signaling Data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 928-936. doi: 10.3969/j.issn.0258-2724.20200086

Citation:

YANG Fei, JIANG Haihang, YAO Zhenxing, LIU Haode. Evaluation of Activity Location Recognition Using Cellular Signaling Data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 928-936. doi: 10.3969/j.issn.0258-2724.20200086

Citation:

PDF( 2094 KB)

Evaluation of Activity Location Recognition Using Cellular Signaling Data

doi: 10.3969/j.issn.0258-2724.20200086

Received Date: 11 Mar 2020
Rev Recd Date: 08 Jun 2020

Available Online: 23 Mar 2021

Publish Date: 15 Oct 2021

Abstract

Abstract

In order to investigate the recognition results of individuals’ activity locations using cellular signaling data, the field experiment for collecting cellular signaling data was carried out. The GPS trajectory data and the travel logs were collected synchronously as the real data for reference. A three-step method for recognizing activity locations is proposed. Firstly, an equal time interval interpolation method is used to balance the time weight of each trace. Secondly, an agglomerative hierarchical clustering algorithm is applied to merge the traces into different clusters. Finally, a new method of correcting location oscillation is proposed, to solve the problem that clusters in the same activity location oscillate. Results show that the proposed method performs well in term of the accuracy of identifying activity locations, distance error and time error. The average recognition accuracy and distance error is over 84% and within 220 m, respectively. The average errors of departure and arrival time are 7.7 min and 5.3 min, respectively. In the comparison of different travel purposes, the activity locations for work receive the best recognition results, and the results of the locations for shopping is relatively inferior.
- traffic survey,
- activity location recognition,
- cellular signaling,
- hierarchical clustering,
- location oscillation correction algorithm

FullText(HTML)

References(23)

References

AHAS R, SILM S, JÄRV O, et al. Using mobile positioning data to model locations meaningful to users of mobile phones[J]. Journal of Urban Technology, 2010, 17(1): 3-27. doi: 10.1080/10630731003597306

ISAACMAN S, BECKER R, CÁCERES R, et al. Identifying important places in people’s lives from cellular network data[C]//International Conference on Pervasive Computing. Berlin, Heidelberg: Springer, 2011: 133-151.

WANG P, HUNTER T, BAYEN A M, et al. Understanding road usage patterns in urban areas[J]. Scientific Reports, 2012, 2: 1-6.

JÄRV O, AHAS R, WITLOX F. Understanding monthly variability in human activity spaces:a twelve-month study using mobile phone call detail records[J]. Transportation Research Part C:Emerging Technologies, 2014, 38: 122-135. doi: 10.1016/j.trc.2013.11.003

XU Y, SHAW S L, ZHAO Z, et al. Another tale of two cities:understanding human activity space using actively tracked cellphone location data[J]. Annals of the American Association of Geographers, 2016, 106(2): 489-502.

WANG Z, HE S Y, LEUNG Y. Applying mobile phone data to travel behaviour research:a literature review[J]. Travel Behaviour and Society, 2018, 11: 141-155. doi: 10.1016/j.tbs.2017.02.005

CALABRESE F, COLONNA M, LOVISOLO P, et al. Real-time urban monitoring using cell phones:a case study in Rome[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 141-151. doi: 10.1109/TITS.2010.2074196

CALABRESE F, DIAO M, DI LORENZO G, et al. Understanding individual mobility patterns from urban sensing data:a mobile phone trace example[J]. Transportation Research Part C:Emerging Technologies, 2013, 26: 301-313. doi: 10.1016/j.trc.2012.09.009

JIANG S, FIORE G A, YANG Y, et al. A review of urban computing for mobile phone traces: Current methods, challenges and opportunities[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery, 2013: 1-9.

WANG F, CHEN C. On data processing required to derive mobility patterns from passively-generated mobile phone data[J]. Transportation Research Part C:Emerging Technologies, 2018, 87: 58-74. doi: 10.1016/j.trc.2017.12.003

HARIHARAN R, TOYAMA K. Project lachesis: parsing and modeling location histories[C]//Interna- tional Conference on Geographic Information Science. Berlin, Heidelberg: Springer, 2004: 106-124.

ALEXANDER L, JIANG S, MURGA M, et al. Origin-destination trips by purpose and time of day inferred from mobile phone data[J]. Transportation Research Part C:Emerging Technologies, 2015, 58: 240-250. doi: 10.1016/j.trc.2015.02.018

WIDHALM P, YANG Y, ULM M, et al. Discovering urban activity patterns in cell phone data[J]. Transportation, 2015, 42(4): 597-623. doi: 10.1007/s11116-015-9598-x

WANG M, CHEN C, MA J. Time-of-day dependence of location variability: application of passively-generated mobile phone dataset[C]//The 94th Annual Meeting of Transportation Research Board. Washington D. C.: [s.n.], 2015: 11-15.

LEE J K, HOU J C. Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application[C]//Proceedings of the 7th ACM International Symposium on Mobile ad Hoc Networking and Computing (MobiHoc). New York: Association for Computing Machinery, 2006: 85-96.

BAYIR M A, DEMIRBAS M, EAGLE N. Mobility profiler:a framework for discovering mobility profiles of cell phone users[J]. Pervasive and Mobile Computing, 2010, 6(4): 435-454. doi: 10.1016/j.pmcj.2010.01.003

SHAD S A, CHEN E. Cell oscillation resolution in mobility profile building[J]. International Journal of Computer Science Issues (IJCSI), 2012, 9(3): 205-213.

QI L, QIAO Y, ABDESSLEM F B, et al. Oscillation resolution for massive cell phone traffic data[C]// Proceedings of the 1st Workshop on Mobile Data. New York: Association for Computing Machinery, 2016: 25-30.

IOVAN C, OLTEANU-RAIMOND A M, COURONNÉ T, et al. Moving and calling: mobile phone data quality measurements and spatiotemporal uncertainty in human mobility studies[J]. Lecture Notes in Geoinformation and Cartography, 2013: 247-265.

DEMISSIE M G, DE ALMEIDA CORREIA G H, BENTO C. Intelligent road traffic status detection system through cellular networks handover information:an exploratory study[J]. Transportation Research Part C:Emerging Technologies, 2013, 32: 76-88. doi: 10.1016/j.trc.2013.03.010

WU W, WANG Y, GOMES J B, et al. Oscillation resolution for mobile phone cellular tower data to enable mobility modelling[C]// Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management. Washington D. C.: IEEE Computer Society, 2014, 1: 317-324.

CHEN C, MA J, SUSILO Y, et al. The promises of big data and small data for travel behavior (aka human mobility) analysis[J]. Transportation Research Part C:Emerging Technologies, 2016, 68: 285-299. doi: 10.1016/j.trc.2016.04.005

CHEN C, BIAN L, MA J. From traces to trajectories:How well can we guess activity locations from mobile phone traces?[J]. Transportation Research Part C:Emerging Technologies, 2014, 46: 326-337. doi: 10.1016/j.trc.2014.07.001

Relative Articles

[1]	ZHU Liuxian, WEI Wei, LI Li. Dimensional Synthesis Approach for Planar Open Path Based on Chord Angle Descriptor[J]. Journal of Southwest Jiaotong University, 2025, 60(3): 688-697. doi: 10.3969/j.issn.0258-2724.20230148
[2]	WANG Yanchen, YANG Fei, LI Rongling, ZHOU Tao. Influence of Location Frequency on Travel Mode Extraction Using Cellular Phone Data[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1158-1166. doi: 10.3969/j.issn.0258-2724.20220136
[3]	HUANG Yihui, YANG Fei, ZHANG Dong, ZENG Youmei. Optimization of Travel Mode Choice Based on MA-CPT Model[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 367-372. doi: 10.3969/j.issn.0258-2724.20210938
[4]	CHEN Rong, CONG Jianli, GAO Mingyuan, WANG Yuan, WANG Ping. Using Smartphone to Detect Vehicle Running Quality and Its Coordinate Alignment[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 830-839. doi: 10.3969/j.issn.0258-2724.20200756
[5]	ZHAO Dongbao, FENG Linlin, DENG Yue, CAO Lianhai. Real-time Online Compression Method for Vehicle Trajectory Data Based on Smart Phone Sensors[J]. Journal of Southwest Jiaotong University, 2022, 57(1): 1-10. doi: 10.3969/j.issn.0258-2724.20210136
[6]	WANG Zhijian, LIU Shijie, ZHOU Jinyao, SUN Jian. Multimodal Public Transportation Route Planning Considering Personalized Travel Demand[J]. Journal of Southwest Jiaotong University, 2022, 57(6): 1319-1325, 1333. doi: 10.3969/j.issn.0258-2724.20210633
[7]	LIU Zhiwei, LIU Jianrong, DENG Wei. Impact of Autonomous Vehicle on Travel Mode Choice Behavior[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1161-1168. doi: 10.3969/j.issn.0258-2724.20200299
[8]	JIANG Xinguo, ZHANG Guopeng, BAI Wei, XIA Liang. Literature Review on Surrogate Exposure Technique: Quasi-induced Exposure[J]. Journal of Southwest Jiaotong University, 2016, 29(3): 546-552. doi: 10.3969/j.issn.0258-2724.2016.03.013
[9]	XIAO Feng, TU Wenwen, CHEN Dong. Recognition of Traffic Congestion Based on Mobile Phone Sensor Data[J]. Journal of Southwest Jiaotong University, 2016, 29(3): 553-562. doi: 10.3969/j.issn.0258-2724.2016.03.014
[10]	LI Shanmei, XU Xiaohao, WANG Chao, WANG Fei. Congestion Identification of Crossing Air Routes Based on Gray Clustering Method[J]. Journal of Southwest Jiaotong University, 2015, 28(1): 189-197. doi: 10.3969/j.issn.0258-2724.2015.01.028
[11]	WANG Chao, HAN Bangcun, WANG Fei. Identification of Prevalent Air Traffic Flow in Terminal Airspace Based on Trajectory Spectral Clustering[J]. Journal of Southwest Jiaotong University, 2014, 27(3): 546-552. doi: 10.3969/j.issn.0258-2724.2014.03.027
[12]	KUANG Ai-Wu, HUANG Zhong-Xiang, KUANG Qun. Evaluation Method for Travel Time Reliability of Road Network with Stochastic Demand[J]. Journal of Southwest Jiaotong University, 2011, 24(5): 861-867. doi: 10.3969/j.issn.0258-2724.2011.05.025
[13]	AN Shi, CUI Na, XU Hang. Personalized Service Strategy of Travel Information Based on Multi-agent Negotiation[J]. Journal of Southwest Jiaotong University, 2010, 23(4): 627-634. doi: 10. 3969/ j. issn. 0258-2724.
[14]	HU Feng, 2, WANG Guoyin, DAI Jin. Quick Discretization Algorithm for Rough Set Based on Dynamic Clustering[J]. Journal of Southwest Jiaotong University, 2010, 23(6): 977-983. doi: 10.3969/j.issn.0258-2724.201
[15]	CHEN Tao-Wei, JIN Wei-Dong, LI Jie. Divisive Hierarchical Clustering Algorithm Based on Grey Relational Measure[J]. Journal of Southwest Jiaotong University, 2010, 23(2): 296-301. doi: 10. 3969/ j. issn. 0258-2724.
[16]	YANG Min, WANG Wei, CHEN Xuewu, WAN Tao, SHANGGUAN Pengcheng. Activity Pattern Choice of Work Commuting Trip by Workers[J]. Journal of Southwest Jiaotong University, 2009, 22(2): 274-279.
[17]	HU Xuegang, CAO Yongzhao, WU Gongqing. Effective Twice-Clustering Algorithm for Data Streams[J]. Journal of Southwest Jiaotong University, 2009, 22(4): 490-494.
[18]	YIN Guisheng, YU Xiang, NING Hui. Incremental Clustering Algorithm Based on Rough Reduction for Data Stream[J]. Journal of Southwest Jiaotong University, 2009, 22(5): 637-642,653.
[19]	ZHAO Yue, DU Wen, CHEN Shuang. Evolving Model for Scale-Free Network Based on Travel Choice[J]. Journal of Southwest Jiaotong University, 2008, 21(4): 531-534.
[20]	YANG Yan, ZHANG Zhaotao. Clustering Method Combining Threshold Algorithm with Ant Colony Algorithm[J]. Journal of Southwest Jiaotong University, 2006, 19(6): 719-722,742.

Supplements(0)

Cited By

Cited by

Periodical cited type(5)

1.	王德，韩滨鹂，张天然，游智敏，李心恬. 手机信令数据的出行测度准确性分析——基于与居民出行调查数据的比较. 地理科学进展. 2024(05): 854-869 .
2.	王殿海，徐望，蔡正义，曾佳棋，黄宇浪. 基于手机信令数据的贝叶斯优化出行链识别. 浙江大学学报(工学版). 2024(11): 2393-2405 .
3.	杨飞，郭煜东，钟宇，刘振国，谭力，王成. 融合多源大数据的区域综合交通需求分析与对策. 交通运输研究. 2023(03): 48-56 .
4.	刘海洲，张敬宇. 基于大数据的城市轨道交通出行站外OD位置点识别方法研究. 铁道运输与经济. 2022(08): 115-122 .
5.	蔡先华，李澳，孙赫扬，李建邺. 运用手机信令数据和综合交通网络的出行链提取. 现代测绘. 2022(05): 18-22 .

Other cited types(11)

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(7) / Tables(2)

Get Citation

PDF

XML

Article views(607) PDF downloads(48)