i-manager Publications

Analysing work tour motifs from GPS Trajectory Data

Jenniffer Joy J*, Dr. Vijaya Samundeeswari**

* Student, Department of Computer Science and Technology, Women's Christian College, Chennai, Tamil Nadu, India.

** Assistant Professor, Department of Computer Science and Technology, Women's Christian College, Chennai, Tamil Nadu, India.

Periodicity:February - April'2018
DOI : https://doi.org/10.26634/jcs.7.2.14442

Abstract

With the objective of gettinga deep insight about the trajectory path and various utilization of GPS trajectory data, a literature review is done on the motifs that can be possibly obtained from the trajectory path. This paper defines one of such ways to model a work tour motif based on the mobility pattern of user’s daily trip from the GPS trajectory path. The segmented trajectory helps in modelling the motif by considering the stay points as motif nodes. By forming such motifs and considering it as a real-world object, various other applications of these data can be coined, which brings out the data from its confinement into a highly influential real-world object. This data analytics can be used to make tracking much easier in traffic synchronizations, forensic case investigations and in various other fields, with the objective of bringing ease of life in each person’s travel routines and reduce the rate of crimes scene happening at thickly populated area.

Keywords

GPS; Trajectory path; Motifs, Satellite Communication;

How to Cite this Article?

Joy, J. J., and Samundeeswari, V. (2018). Analysing Work Tour Motifs from GPS Trajectory Data. i-manager’s Journal on Communication Engineering and Systems, 7(2), 31-36. https://doi.org/10.26634/jcs.7.2.14442

References

1]. Cheung, V., & Cannons, K. (2003). An Introduction to Probabilistic Neural Networks. Signal & Data Compression Laboratory, Electrical & Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada.

[2]. David, K. (2005). A Brief Introduction to Neural Networks. Retrieved from http://www.dkriesel.com/_media/science/ neuronalenetze-en-zeta2-2col-dkrieselcom.pdf

[3]. Jiang, S., Ferreir, J., & Gonzalez, M. C. (2012). Clustering daily patterns of human activities in the city. Data Mining and Knowledge Discovery, 25(3), 478-510.

[4]. Jiang, S., Ferreir, J., & Gonzalez, M. C. ( 2017). Activitybased human mobility patterns inferred phone data: A case study on Singapore, IEEE Transactions on Big Data, 3(2), 208-219.

[5]. Jiang, S., Fiore, G. A., Yang, Y., Ferreira Jr, J., Frazzoli, E., & González, M. C. (2013, August). A review of urban computing for mobile phone traces: Current methods, nd challenges and opportunities. In Proceedings of the 2 ACM SIGKDD International Workshop on Urban Computing (p. 2). ACM.

[6]. Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C. (2013). Unravelling daily human mobility motifs. Journal of the Royal Society Interface, 10(84). DOI: 10.1098/rsif.2013.0246.

[7]. T. R. Board, (2015). Activity-Based Travel Demand Models: A Primer. TRB Publications.

[8]. Xu, X. (1990). RT-tree: An improved R-tree index th structure for spatiotemporal databases. In Proc. of the 4 Intl. Symposium on Spatial Data Handling.

[9]. Zheng, Y. (2015). Trajectory data mining: An overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3).

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Analysing work tour motifs from GPS Trajectory Data

Abstract

Keywords

How to Cite this Article?

References

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