The week's pick
Random Articles
Reseach Article
A Novel Approach for Mining Trajectory Patterns of Moving Vehicles
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 104 - Number 4 |
| Year of Publication: 2014 |
| Authors: Vaishali Mirge, Shubhrata Gupta, Keshri Verma |
10.5120/18188-9097
|
Vaishali Mirge, Shubhrata Gupta, Keshri Verma . A Novel Approach for Mining Trajectory Patterns of Moving Vehicles. International Journal of Computer Applications. 104, 4 ( October 2014), 4-8. DOI=10.5120/18188-9097
Abstract
With the advances in location- acquisition technologies such as Global Positioning System (GPS), Global System for Mobile Communications (GSM) etc, increasing amounts of movement data collected from various moving objects such as animals, vehicles, mobile devices, and climate radars have become widely available. Turning a collection of time-geography data into mobility knowledge is a key issue in many research domain. In this article we focus on the analysis of trajectories of moving vehicles and identifying the paths on road networks, which are suffering with heavy traffic. This paper proposes a novel algorithm to get trajectory patterns as a sequence of spatio-temporal regions. These sequences specify the paths heavily loaded with vehicles in certain duration. The problem domain is divided into two parts, First in order to discover spatio-temporal regions, the data space is partitioned into meaningful clusters called spatio-temporal regions. Second, arranged the spatio-temporal regions according to ascending order of time to obtain the trajectory patterns, which shows the paths frequently followed by most of the vehicles in certain duration i. e. path suffered with heavy traffic.
References
- Tsoukatos, I. and Gunopulos, D. 2001. Efficient Mining Spatio Temporal Pattern. Proceedings of International Symposium in Spatial and Temporal Databases. 425-442.
- Cheng, H. , Yan X. , Han J. Philip S. 2008. Direct Discriminative Pattern Mining for Effective Classification. ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering.
- Xiaowei, X. , Martin, E. , Kriegel, H. , and Sander, J. 1998. A Distribution-Based clustering Algorithm for Mining in Large Spatial Databases. Proceedings of 14th International Conference on Data Engineering (ICDE). 169-178.
- Ester M. , Kriegel H. , Sandar J. , Xu X. and Kut, A. 1998. A Density-Based Clustering in Spatial Databases : The Algorithm GDBSCAN and Its Applications. Data Mining & Knowledge Discovery. Volume 2. Issue 2. 169-194.
- Li,Z. , Ji,M. , Lee, J-G. ,Tang, L. , Han, J. , and Kays, R. . 2010. MoveMine: Mining Moving Object Databases. International Conference on Management of Data.
- Agrawal R. and Srikant, R. 1995. Mining Sequential Patterns. Proc. 1995 International Conference on Data Engineering (ICDE ). 3-14.
- Giannotti F. , M. Nanni, Pedreschi, D. and Pinelli, F. 2007. Trajectory Pattern Mining, KDD?07, August 12-15, San Jose, California, USA. 330-339
- H. Cheng, X. Yan, J. Han, and S. Philip, "Direct Discriminative Pattern Mining for Effective Classification", ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, 2008.
- W. Wang, J. Yang, and R. Muntz, "STING: A Statistical Information Grid Approach to Spatial Data Mining", In Proc. 23rd Int'l Conf. on Very Large DataBases, Athens, Greece, pp. 186-195, Aug 1997.
- J. Kang and H. Yong "Mining Trajectory Patterns by Incorporating Temporal Properties", In Proceedings of the 1st International Conference on Emerging Databases (EDB2009), 2009.
Index Terms
Keywords