International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 5 | Number 2 | Year 2013 | Article Id. IJCTT-V5N4P135 | DOI : https://doi.org/10.14445/22312803/IJCTT-V5N4P135

Analyzing the Road Traffic and Accidents with Classification Techniques

M. Sowmya , Dr.P. Ponmuthuramalingam

Citation :

M. Sowmya , Dr.P. Ponmuthuramalingam, "Analyzing the Road Traffic and Accidents with Classification Techniques," International Journal of Computer Trends and Technology (IJCTT), vol. 5, no. 2, pp. 183-188, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V5N4P135

Abstract

Data mining is the process of extracting data’s from the database engines. Now a days the road traffic and accidents are main area for the researchers to discover the new problems behind that. It is commonly used in a marketing, inspection, fraud detection and scientific invention. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays road accidents are the major causes of death and injuries in this world. Roadway configurations are useful in the development of traffic safety control policy. Spatial data mining is a research area concerned with the identification of interesting spatial patterns from data stored in spatial databases and geographic information systems (GIS). This paper addresses the analysis of spatial and time stamped data of Slovenian traffic accidents which, together with the GIS data, enabled the construction of spatial attributes and the creation of a time-stamped spatial database.

Keywords

Mining, road traffic, Data mining, spatial databases

References

[1]  Beshah, T. and S. Hill. Mining road traffic accident data to improve safety: Role of road-related factors on accident severity in Ethiopia. 2010. 
[2]  Lavrac, N., et al., Mining spatio-temporal data of traffic accidents and spatial pattern visualization. Metodoloski zveski, 2008. 5(1): p. 45-63. 
[3]  Tran, T.N., R. Wehrens, and L.M.C. Buydens, KNN-kernel density-based clustering for highdimensional multivariate data. Computational Statistics & Data Analysis, 2006. 51: p. 513-525. 
[4]  Inselberg, A. and B. Dimsdale. Parallel coordinates: A tool for visualizing multi-dimensional geometry. in IEEE Visualization. 1990. 
[5]  Fukunaga, K. and L. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition. Information Theory, IEEE Transactions on, 1975. 21(1): p. 32-40. 
[6]  Das, S., M. Lazarewicz, and L.H. Finkel. Principal Component Analysis of Temporal and Spatial Information for Human Gait Recognition. in The 26th Annual International Conference of IEEE EMBS. 2004. San Francisco, CA, USA: IEEE.  [7]  Geovisualization Guo, D., et al., Multivariate Analysis and with an Integrated Geographic Knowledge Discovery Approach. Cartographic and Geographic Information Science, 2005. 32(2): p. 113-132. 
[8]  Skupin, A., The world of geography: Visualizing a knowledge domain with cartographic means. 2004, PNAS. p. 5274-5278. 
[9] Gorricha, J. and V. Lobo, Improvements on the visualization of clusters in geo-referenced data using Self-Organizing Maps. Computers and Geosciences, 2012. 43: p. 177-186. 
[10]  Rousseeuw, P.J., Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 1987. 20(C): p. 53-65.