[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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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[19] |
Worton B J. Kernel methods for estimating the utilization distribution in home-range studies[J]. Ecology, 1989: 70(1):164-168.
|
[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.
|