Evaluation Clustering Method to Discover Crowd Movement within City Transportation
Abstract
consists of multiple objects. To find out the crowdedness area of
mobile vehicle in particular area in city is necessary for control
the traffic condition. Initial step for this is to cluster the object
in that area to receive the density information. The practical
detecting crowdedness spots in city consisting of many unique
features such as highly mobile surrounding, Greatest possible
restricted size of sample objects, and biased samples which is
not uniform, and all these features have to built new challenges
due to which traditional density-based clustering algorithms is
deficient to retrieve the genuine clustering property of objects
and produce output less meaningful. To provide proper solution
a novel non density based technique also knows as mobility-based
clustering can be used.
Full Text:
PDFReferences
Siyuan Liu, Yunhuai Liu, Lionel Ni, Minglu Li and Jianping Fan,
”Detecting crowdedness spot in city transportation” , IEEE Transact ions
On Vehicular Technology, , pp. 1527-1539,Vol. 62, NO. 4, May 2013.
S. Liu, Y. Liu, L. Ni, J. Fan, and M. Li,” Towards mobility-based
clustering”, Proc. ACM SIGKDD, pp. 919928, 2010.
J. Sander, M. Ester, H.-P. Kriegel, and X. Xu,” Density-based clustering
in spatial databases: The algorithm gdbscan and its applications” , Data
Min.Knowl. Discov., vol. 2, no. 2, pp. 169194, Jun. 1998.
Y. Li, J. Han, and J. Yang, ”Clustering moving objects” , Proc. ACM
SIGKDD, pp. 617622, 2004.
H. Yoon and C. Shahabi, ”Robust time-referenced segmentation of
moving object trajectories” , Proc. IEEE ICDM, pp. 11211126, 2008.
D. Chakrabarti, R. Kumar, and A. Tomkins, ”Evolutionary clustering ” ,
Proc. ACM SIGKDD, pp. 554560, 2006.
P . S. Castro, D. Zhang, and S. Li, ”Urban traffic modelling and predict
ion using large scale taxi GPS traces ”, in Proc. Pervasive, pp. 5772,
Refbacks
- There are currently no refbacks.
Copyright © IJETT, International Journal on Emerging Trends in Technology