Classifier prediction evaluation in modeling road traffic accident data

This paper illustrates the research work in exploring the application of data mining techniques to aid in the prediction of road accident patterns related to pedestrian characteristics. It also provides insight into pedestrian accidents by uncovering their patterns and their recurrent underlying cha...

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Bibliographic Details
Published in2012 IEEE International Conference on Computational Intelligence and Computing Research pp. 1 - 4
Main Authors Ramani, R. Geetha, Shanthi, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Accident patterns
Casualties
Cross Validation
Decision Tree
Pedestrians
Road Traffic Accidents
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Summary:This paper illustrates the research work in exploring the application of data mining techniques to aid in the prediction of road accident patterns related to pedestrian characteristics. It also provides insight into pedestrian accidents by uncovering their patterns and their recurrent underlying characteristics in order to design defensive measures and to allocate resources for identified problems. In this study the Decision Tree algorithms viz. Random Tree, C4.5, J48 and Decision Stump are applied to a database of fatal accidents occurred during the year 2010 in Great Britain. We also used K-folds Cross-Validation methods to measure the unbiased estimate of the four prediction models for performance comparison purposes.
ISBN:1467313424
9781467313421
DOI:10.1109/ICCIC.2012.6510289