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Reseach Article

Classifying Trajectories on Road Network using Neural Network

by Deepak S. Gaikwad, Usha A. Jogalekar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 13
Year of Publication: 2013
Authors: Deepak S. Gaikwad, Usha A. Jogalekar
10.5120/14072-2319

Deepak S. Gaikwad, Usha A. Jogalekar . Classifying Trajectories on Road Network using Neural Network. International Journal of Computer Applications. 81, 13 ( November 2013), 14-16. DOI=10.5120/14072-2319

@article{ 10.5120/14072-2319,
author = { Deepak S. Gaikwad, Usha A. Jogalekar },
title = { Classifying Trajectories on Road Network using Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 13 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 14-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number13/14072-2319/ },
doi = { 10.5120/14072-2319 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:58.543208+05:30
%A Deepak S. Gaikwad
%A Usha A. Jogalekar
%T Classifying Trajectories on Road Network using Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 13
%P 14-16
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification is very important in the process of Machine Learning and Data Mining. Traditional Neural Network classifier work with many kinds of data such as items, text documents, signals, networks, but there is lack of study on Trajectory Classification based on Neural Network. In this paper, proposing a system for classification of Trajectories on Road Network using classifier Neural Network. In this paper explored classification technique used for Trajectory Classification. The best feature candidate for classifying trajectory on road network is Sequential Pattern, as it preserves order of visiting sequence of Trajectories on road network. In this paper, here proposing a model using sequential pattern and neural network for acquiring high accuracy and efficiency for Trajectory Classification.

References
  1. Jae-Gil Lee, Jiawei Han, Fellow,Xiaolei Li, and Hong Cheng, "Mining Discriminative Patterns for Classifying Trajectories on Road Networks",IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 5, MAY 2011
  2. G. Gido falvi and T. B. Pedersen, Mining Long, Sharable Patterns in Trajectories of Moving Objects, GeoInformatica, vol. 13, no. 1, pp. 27-55, 2009. 724 IEEE TRANS-ACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 5, MAY 2011 TABLE 8 The Summary of the Notation Fig. 15. Estimation of the information gain as k varies.
  3. J. Gudmundsson and M. J. Kreveld, Computing Longest Duration Flocks in Trajectory Data, Proc. 14th ACM Intl Symp. Geographic Information Systems, pp. 35-42, Nov. 2006. K. Elissa, "Title of paper if known," unpublished.
  4. N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, and D. W. Cheung, Mining, Indexing, and Querying Historical Spatiotemporal Data, Proc. ACM SIGKDD, pp. 236-245, Aug. 2004.
  5. Y. Zheng, L. Zhang, X. Xie, and W. -Y. Ma, Mining Interesting Locations and Travel Sequences from GPS Trajectories, Proc. 18th Intl Conf. World Wide Web, pp. 791- 800, Apr. 2009.
  6. T. Brinkhoff, A Framework for Generating Network-Based Moving Objects, GeoInformatica, vol. 6, no. 2, pp. 153-180, 2002.
  7. R. Agrawal and R. Srikant, Mining Sequential Patterns, Proc. 11th Intl Conf. Data Eng. , pp. 3-14, Mar. 1995.
  8. X. Yan, J. Han, and R. Afshar, "CloSpan: Mining Closed Sequential Patterns in Large Databases," Proc. Third SIAM Int'l Conf. Data Mining, May 2003.
  9. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins, "Text Classification Using String Kernels," J. Machine Learning Research, vol. 2, pp. 419-444, 2002.
  10. C. S. Leslie, E. Eskin, and W. S. Noble, "The Spectrum Kernel: A String Kernel for SVM Protein classification," Proc. Seventh Pacific Symp. Biocomputing, pp. 566-575, Jan. 2002.
  11. M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis, "Frequent Substructure-Based Approaches for Classifying Chemical Compounds," IEEE Trans. Knowledge and Data Eng. , vol. 17, no. 8, pp. 1036- 1050, Aug. 2005.
  12. L. R. Rabiner and B. H. Juang, "An Introduction to Hidden Markov Models," IEEE ASSP Magazine, vol. 3, no. 1, pp. 4-16, Jan. 1986.
  13. R. Fraile and S. J. Maybank, "Vehicle Trajectory Approximation and Classification," Proc. Ninth British Machine Vision Conf. , pp. 832-840, Sept. 1998.
  14. Sun, G. Z. ; Chen, H. H. ; Lee, Y. C. ; Liu, Y. D. Time warping recurrent neural networks and trajectory classification, Neural Networks, 1992. IJCNN. International Joint Conference. Volume: 1 Publication Year: 1992 , Page(s): 431 - 436 vol. 1
  15. Stephen Gang Wu, Forrest Sheng Bao, Eric You Xu, Yu-Xuan Wang, Yi-Fan Chang and Qiao-Liang Xiang, "A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network", Signal Processing and Information Technology, 2007 IEEE International Symposium, pp. 11-16 Dec 2007.
  16. A. Fehske, J. Gaeddert and J. H. Reed, "A New Approach to Signal Classification Using Spectral Correlation and Neural Networks", New Frontiers in Dynamic Spectrum Access Networks, 2005, DySPAN 2005. 2005 First IEEE International Symposium on, Publication Year: 2005, Page(s): 144 - 150.
  17. Guobin Ou, Yi Lu Murphey, and Lee Feldkamp, "Multiclass Pattern Classification Using Neural Networks", Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Volume: 4), pp. 585 - 588 Vol. 4.
  18. C. -C. Chang and C. -J. Lin, LIBSVM: A Library for Support Vector Machines, http://www. csie. ntu. edu. tw/ cjlin/libsvm, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Classifier Trajectory Classification.