A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable resul...

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Bibliographic Details
Published in:Mathematical problems in engineering 2013-01, Vol.2013 (2013), p.1-8
Main Authors: Ding, Tong-qiang, Niu, Shifeng, Gao, Zhenhai, Xi, Jian-feng, Ning, Guobao
Format: Article
Language:English
Subjects:
Accident analysis
Accident data
Algorithms
Correlation analysis
Data analysis
Data mining
Data sampling
Delphi method
Entropy
Forensic engineering
International conferences
Optimization algorithms
Particle swarm optimization
Studies
Test procedures
Traffic accidents
Traffic accidents & safety
Traffic speed
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Summary:Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable.
ISSN:1024-123X
1563-5147
DOI:10.1155/2013/302627