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Study on the Method and Application of Big Data Mining of Mobile Trajectory Based on MapReduce

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DOI: 10.23977/jaip.2020.030103 | Downloads: 7 | Views: 439


Jiatong Han 1


1 Hohhot Municipal Engineering Technology Service Center, Inner Mongolia, 010023, China

Corresponding Author

Jiatong Han


In the era of mapreduce when “Internet +” is developed to “Big data x”, big data has gradually become a research focus closely followed by the scientific and technological circle, industry circle, and government departments. Big data analysis for moving taxi trajectory has gradually become a research hotspot in the fields of smart city information computing and smart city construction. At present, social problems such as traffic congestion, environmental degradation, and energy shortages are seriously affecting the safe and livable development of smart cities and their sustainable development. Through deep mining, analysis and comprehensive utilization of taxi trajectory data based on geographic location in the mobile social taxi network, it provides a new idea for the analysis of complex urban public transportation problems. This paper will focus on the new data analysis method and its practical application of deep analysis and mining of mobile taxi trajectory big data based on mapreduce. It’s dedicated to effectively solve the three major problems of data, including the real-time, robustness and accuracy, and provides theoretical basis and relevant practical technology for the application of urban dynamic monitoring and early warning control of complex urban public transportation network.


Big data analysis, MapReduce, Trajectory mining


Jiatong Han. Study on the Method and Application of Big Data Mining of Mobile Trajectory Based on MapReduce. Journal of Artificial Intelligence Practice (2020) Vol. 3: 9-12. DOI:


[1] Xia Dawen. Research on Method and Application of Big Data Mining of Mobile Track Based on MapReduce [D]. 2016.
[2] Yang Jie, Li Xiaoping, Chen Tian. Group mining method based on incremental spatiotemporal trajectory big data [J]. Computer Research and Development, 2014 (S2): 76-85.
[3] Li Xin. Mining trajectory adjoint patterns based on time-space segmentation and word vector similarity [J]. Journal of Sun Yat-Sen University, 2019, 58: 5, 2019, 58 (5): 17-25

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