ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Discovery of hot regions about crowd activities based on mobility data

  • School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and technology, Shanghai 200093, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.10.005
  • Received Date: 27 August 2015
  • Accepted Date: 29 September 2015
  • Rev Recd Date: 29 September 2015
  • Publish Date: 30 October 2015
    Abstract

  • Abstract

    Mobility data records the change of location and time about crowd activities, showing semantic knowledge about human mobility. From the perspective of regional semantic knowledge, mining the hot regions visited frequently by moving crowds is essential to understand regional characteristics in the smart city applications. This paper studied how to discover hot regions and how to constraint their coverage size. Based on an analysis of the location sequence of moving crowd, a discovery method for discovering hot regions based on kernel function was proposed. This discovery method uses the grid as a spatial data indexing structure and the Top-k sorting method. A discovery algorithm of hot regions was presented based on the discovery method. Finally, experimental results validate accurately the feasibility and effectiveness of the method on practical datasets.

    Abstract

    Mobility data records the change of location and time about crowd activities, showing semantic knowledge about human mobility. From the perspective of regional semantic knowledge, mining the hot regions visited frequently by moving crowds is essential to understand regional characteristics in the smart city applications. This paper studied how to discover hot regions and how to constraint their coverage size. Based on an analysis of the location sequence of moving crowd, a discovery method for discovering hot regions based on kernel function was proposed. This discovery method uses the grid as a spatial data indexing structure and the Top-k sorting method. A discovery algorithm of hot regions was presented based on the discovery method. Finally, experimental results validate accurately the feasibility and effectiveness of the method on practical datasets.
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    • References(19)
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    Smith G, Wieser R, Goulding J, et al. A refined limit on the predictability of human mobility[C]// IEEE International Conference on Pervasive Computing and Communications. Budapest, Hungary: IEEE Press, 2014: 88-94.
    [2]
    Lin M, Hsu W J, Lee Z Q. Predictability of individuals' mobility with high-resolution positioning data[C]// Proceedings of the ACM Conference on Ubiquitous Computing. London: ACM Press, 2012: 381-390.
    [3]
    Qiao S J, Shen D Y, Wang X T, et al. A self-adaptive parameter selection trajectory prediction approach via hidden Markov models[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(1): 284-296.
    [4]
    Qiao S J, Han N, Zhu W, et al. TraPlan: An effective three-in-one trajectory-prediction model in transportation networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 16(3): 1188-1198.
    [5]
    Houenou A, Bonnifait P, Cherfaoui V, et al. Vehicle trajectory prediction based on motion model and maneuver recognition[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan: IEEE Press, 2013: 4363-4369.
    [6]
    Yuan J, Zheng Y, Xie X. Discovering regions of different functions in a city using human mobility and POIs[C]// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing, China: ACM Press, 2012:186-194.
    [7]
    Giannotti F, Nanni M, Pedreschi D, et al. Trajectory pattern mining[C]// Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, USA: ACM Press, 2007:330-339.
    [8]
    刘奎恩, 肖俊超, 丁治明, 等. 轨迹数据库中热门区域的发现[J]. 软件学报, 2013, 24(8): 1816-1835.
    Liu K E, Xiao J C, Ding Z M, et al. Discovery of hot region in trajectory databases[J]. Journal of Software, 2013, 24(8):1816-1835
    [9]
    Pan G, Qi G D, Zhang W S, et al. Trace analysis and mining for smart cities: Issues, methods, and applications[J]. IEEE Communications Magazine, 2013, 51(6): 120-126.
    [10]
    Zadegan S M R, Mirzaie M, Sadoughi F. Ranked k-medoids: A fast and accurate rank-based partitioning algorithm for clustering large datasets[J]. Knowledge-Based Systems, 2013, 39:133-143.
    [11]
    Wu O, Hu W M, Maybank S J, et al. Efficient clustering aggregation based on data fragments[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2012, 42(3): 913-926.
    [12]
    Shi J M, Mamoulis N, Wu D M, et al. Density-based place clustering in geo-social networks[C]// Proceedings of the ACM SIGMOD International Conference on Management of Data. Snowbird, USA: ACM Press, 2014: 99-110.
    [13]
    Liu S Y, Liu Y H, Ni L M, et al. Towards mobility-based clustering[C]// Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, USA: ACM Press, 2010: 919-928.
    [14]
    Dai J. A novel moving object trajectories clustering approach for very large datasets[C]// Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering. Paris, France: Atlantis Press, 2013: 863-866.
    [15]
    Patel J M, Chen Y, Chakka V P. STRIPES: An efficient index for predicted trajectories[C]// Proceedings of the ACM SIGMOD International Conference on Management of Data. Paris, France: ACM Press, 2004:635-646.
    [16]
    Mamoulis N, Cao H P, Kollios G, et al. Mining, indexing, and querying historical spatiotemporal data[C]// Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, USA: ACM Press, 2004: 236-245.
    [17]
    Jensen C S, Lin D, Chin B, et al. Effective density queries on continuously moving objects[C]// Proceedings of the 22nd International Conference on Data Engineering. Atlanta, USA: IEEE Computer Society, 2006: 1-11.
    [18]
    刘奎恩, 丁治明, 李明树. MOIR/HR: 覆盖区域受限的热门区域挖掘[J]. 计算机研究与发展, 2010, 47(z1): 455-458.
    Liu K E, Ding Z M, Li M S. MOIR/HR: Mining of hot regions with coverage constraints[J]. Journal of Computer Research and Development, 2010, 47(z1): 455-458.
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    Worton B J. Kernel methods for estimating the utilization distribution in home-range studies[J]. Ecology, 1989: 70(1):164-168.
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    [1]
    Smith G, Wieser R, Goulding J, et al. A refined limit on the predictability of human mobility[C]// IEEE International Conference on Pervasive Computing and Communications. Budapest, Hungary: IEEE Press, 2014: 88-94.
    [2]
    Lin M, Hsu W J, Lee Z Q. Predictability of individuals' mobility with high-resolution positioning data[C]// Proceedings of the ACM Conference on Ubiquitous Computing. London: ACM Press, 2012: 381-390.
    [3]
    Qiao S J, Shen D Y, Wang X T, et al. A self-adaptive parameter selection trajectory prediction approach via hidden Markov models[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(1): 284-296.
    [4]
    Qiao S J, Han N, Zhu W, et al. TraPlan: An effective three-in-one trajectory-prediction model in transportation networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 16(3): 1188-1198.
    [5]
    Houenou A, Bonnifait P, Cherfaoui V, et al. Vehicle trajectory prediction based on motion model and maneuver recognition[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan: IEEE Press, 2013: 4363-4369.
    [6]
    Yuan J, Zheng Y, Xie X. Discovering regions of different functions in a city using human mobility and POIs[C]// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing, China: ACM Press, 2012:186-194.
    [7]
    Giannotti F, Nanni M, Pedreschi D, et al. Trajectory pattern mining[C]// Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, USA: ACM Press, 2007:330-339.
    [8]
    刘奎恩, 肖俊超, 丁治明, 等. 轨迹数据库中热门区域的发现[J]. 软件学报, 2013, 24(8): 1816-1835.
    Liu K E, Xiao J C, Ding Z M, et al. Discovery of hot region in trajectory databases[J]. Journal of Software, 2013, 24(8):1816-1835
    [9]
    Pan G, Qi G D, Zhang W S, et al. Trace analysis and mining for smart cities: Issues, methods, and applications[J]. IEEE Communications Magazine, 2013, 51(6): 120-126.
    [10]
    Zadegan S M R, Mirzaie M, Sadoughi F. Ranked k-medoids: A fast and accurate rank-based partitioning algorithm for clustering large datasets[J]. Knowledge-Based Systems, 2013, 39:133-143.
    [11]
    Wu O, Hu W M, Maybank S J, et al. Efficient clustering aggregation based on data fragments[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2012, 42(3): 913-926.
    [12]
    Shi J M, Mamoulis N, Wu D M, et al. Density-based place clustering in geo-social networks[C]// Proceedings of the ACM SIGMOD International Conference on Management of Data. Snowbird, USA: ACM Press, 2014: 99-110.
    [13]
    Liu S Y, Liu Y H, Ni L M, et al. Towards mobility-based clustering[C]// Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, USA: ACM Press, 2010: 919-928.
    [14]
    Dai J. A novel moving object trajectories clustering approach for very large datasets[C]// Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering. Paris, France: Atlantis Press, 2013: 863-866.
    [15]
    Patel J M, Chen Y, Chakka V P. STRIPES: An efficient index for predicted trajectories[C]// Proceedings of the ACM SIGMOD International Conference on Management of Data. Paris, France: ACM Press, 2004:635-646.
    [16]
    Mamoulis N, Cao H P, Kollios G, et al. Mining, indexing, and querying historical spatiotemporal data[C]// Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, USA: ACM Press, 2004: 236-245.
    [17]
    Jensen C S, Lin D, Chin B, et al. Effective density queries on continuously moving objects[C]// Proceedings of the 22nd International Conference on Data Engineering. Atlanta, USA: IEEE Computer Society, 2006: 1-11.
    [18]
    刘奎恩, 丁治明, 李明树. MOIR/HR: 覆盖区域受限的热门区域挖掘[J]. 计算机研究与发展, 2010, 47(z1): 455-458.
    Liu K E, Ding Z M, Li M S. MOIR/HR: Mining of hot regions with coverage constraints[J]. Journal of Computer Research and Development, 2010, 47(z1): 455-458.
    [19]
    Worton B J. Kernel methods for estimating the utilization distribution in home-range studies[J]. Ecology, 1989: 70(1):164-168.
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