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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Main Authors:	Jianfeng Xi, Zhenhai Gao, Shifeng Niu, Tongqiang Ding, Guobao Ning
Format:	Article
Language:	English
Published:	Hindawi Limited 2013-01-01
Series:	Mathematical Problems in Engineering
Online Access:	http://dx.doi.org/10.1155/2013/302627

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spelling	doaj-c5e2143d70234fa290a0fa65991ee7e22020-11-24T23:07:16ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/302627302627A Hybrid Algorithm of Traffic Accident Data Mining on Cause AnalysisJianfeng Xi0Zhenhai Gao1Shifeng Niu2Tongqiang Ding3Guobao Ning4State Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun 130022, ChinaSchool of Automobile, Chang’an University, Xi’an 710064, ChinaCollege of Traffic, Jilin University, Changchun 130022, ChinaSchool of Automotive Engineering, Tongji University, Shanghai 201804, ChinaRoad 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.http://dx.doi.org/10.1155/2013/302627
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Jianfeng Xi Zhenhai Gao Shifeng Niu Tongqiang Ding Guobao Ning
spellingShingle	Jianfeng Xi Zhenhai Gao Shifeng Niu Tongqiang Ding Guobao Ning A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis Mathematical Problems in Engineering
author_facet	Jianfeng Xi Zhenhai Gao Shifeng Niu Tongqiang Ding Guobao Ning
author_sort	Jianfeng Xi
title	A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis
title_short	A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis
title_full	A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis
title_fullStr	A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis
title_full_unstemmed	A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis
title_sort	hybrid algorithm of traffic accident data mining on cause analysis
publisher	Hindawi Limited
series	Mathematical Problems in Engineering
issn	1024-123X 1563-5147
publishDate	2013-01-01
description	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.
url	http://dx.doi.org/10.1155/2013/302627
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A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

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