Discovery of hot regions about crowd activities based on mobility data

doi:10.3969/j.issn.0253-2778.2015.10.005

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.

Key words: mobility data, crowd activities, hot region, grid indexing, density, kernel function

CLC Number:

TP18

BAN Leiyu, HUO Huan, XU Biao. Discovery of hot regions about crowd activities based on mobility data[J]. Journal of University of Science and Technology of China, 2015, 45(10): 829-835.

References

［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.(下转第863页)

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