Self-adaptive trajectory prediction model for moving objects in big data environment

被引：0

作者：

Qiao, Shao-Jie ^{[1
]}

Li, Tian-Rui ^{[1
]}

Han, Nan ^{[2
]}

Gao, Yun-Jun ^{[3
]}

Yuan, Chang-An ^{[4
]}

Wang, Xiao-Teng ^{[1
]}

Tang, Chang-Jie ^{[5
]}

机构：

[1] School of Information Science and Technology, Southwest Jiaotong University, Chengdu

[2] School of Life Science and Engineering, Southwest Jiaotong University, Chengdu

[3] College of Computer Science and Technology, Zhejiang University, Hangzhou

[4] Science Computing and Intelligent Information Processing of Guangxi Higher Education Key Laboratory (Guangxi Teachers Education University), Nanning

[5] College of Computer Science, Sichuan University, Chengdu

来源：

Ruan Jian Xue Bao/Journal of Software | 2015年 / 26卷 / 11期

关键词：

Hidden Markov model; Intelligent transportation; Location big data; Self-adaptive; Trajectory prediction;

D O I：

10.13328/j.cnki.jos.004889

中图分类号：

学科分类号：

摘要：

The existing trajectory prediction algorithms focus on the mobility pattern of objects and simulate the traffic flow via mathematical models which are inaccurate at describing network-constraint objects. In order to cope with this problem, a self-adaptive parameter selection trajectory prediction model based on hidden Markov models (SATP) is proposed. The new model can efficiently cluster and partition location big data, and extract the hidden and observable states by using a density-based clustering approach in order to reduce the number of states in HMM. SATP can automatically select the parameters on the input trajectories and avoid the problems of discontinuous hidden states and state retention. Experimental results demonstrate that the SATP model has high prediction accuracy with less time overhead. The average prediction accuracy of SATP is 84.1% while the moving objects have a random changing speed, which is higher than the Naïve algorithm with an average gap of 46.7%. © Copyright 2015, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

引用

页码：2869 / 2883

页数：14

共 20 条

[1] Zhang J.P., Wang F.Y., Wang K.F., Lin W.H., Xu X., Chen C., Data-Driven intelligent transportation systems: A survey, IEEE Trans. on Intelligent Transportation Systems, 12, 4, pp. 1624-1639, (2011)
[2] Wang Z.C., Lu M., Yuan X.R., Zhang J.P., Wetering H., Visual traffic jam analysis based on trajectory data, IEEE Trans. on Visualization and Computer Graphics, 19, 12, pp. 2159-2168, (2013)
[3] Meng X.F., Ding Z.M., Mobile Data Management: Concepts and Techniques, pp. 185-200, (2009)
[4] Guo C., Liu J.N., Fang Y., Luo M., Cui J.S., Value extraction and collaborative mining methods for location big data, Ruan Jian Xue Bao/Journal of Software, 25, 4, pp. 713-730, (2014)
[5] Calabrese F., Pereira F.C., Francisco C., Di Lorenzo G., Liu L., Ratti C., The geography of taste: Analyzing cell-phone mobility and social events, Proc. of the 8th Int'l Conf. on Pervasive Computing, pp. 22-37, (2010)
[6] Calabrese F., Smoreda Z., Blondel V.D., Ratti C., Interplay between telecommunications and face-to-face interactions: A study using mobile phone data, PLoS ONE, 6, 7, (2011)
[7] Qiao S.J., Han N., Wang C., Zhu F., Tang C.J., A two-tiered dynamic index structure of moving objects based on constrained networks, Chinese Journal of Computers, 37, 9, pp. 1947-1958, (2014)
[8] Monreale A., Pinelli F., Trasarti R., Giannotti F., WhereNext: A location predictor on trajectory pattern mining, Proc. of the 15th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pp. 637-646, (2009)
[9] Ying J.J., Lee W., Weng T., Tseng V.S., Semantic trajectory mining for location prediction, Proc. of the 19th ACM SIGSPATIAL Int'l Conf. on Advances in Geographic Information Systems, pp. 34-43, (2011)
[10] Song M.B., Ryu J.H., Lee S.K., Hwang C.S., Considering mobility patterns in moving objects database, Proc. of the 2003 Int'l Conf. on Parallel Processing, pp. 597-604, (2003)

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