Structure-Based Rule Selection Framework for Association Rule Mining of Traffic Accident Data

Marukatat, Rangsipan

doi:10.1007/978-3-540-74377-4_25

Cited by 10 publications

(6 citation statements)

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“…Carry (10) and (12) into (9), the entropy value of traffic accident attribute importance is calculated as such:…”

Section: Calculation Of Accident Causation Attributes Associationmentioning

confidence: 99%

“…Association rules has captured wide attentions and careful studies because of its adoptability and the nature of being easily understood, the focus of study of which is how to increase the accuracy and efficiency of the calculation. Among the researches to date, Geurts et al [7] used association rules to identify accident circumstances that frequently occur together at high frequency accident locations; Tesema et al [8] developed an adaptive regression trees to build a decision support system to handle road traffic accident analysis; Marukatat [9] has made noticeable attempts at identifying the degree of importance of Information Entropy for road traffic accident analyses. Dong et al [10], Lee et al [11], Hassan and Tazaki [12], Zhang et al [13], and other researchers have achieved multileveled data mining of traffic accidents by means of a comprehensive application of data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

Gao

Niu

et al. 2013

Mathematical Problems in Engineering

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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.

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“…Carry (10) and (12) into (9), the entropy value of traffic accident attribute importance is calculated as such:…”

Section: Calculation Of Accident Causation Attributes Associationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

Gao

Niu

et al. 2013

Mathematical Problems in Engineering

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“…The details are as follows. (Marukatat, 2007), criteria to determine whether a rule is semantically useful are as follows.…”

Section: Semantic Classification and Pattern Analysismentioning

confidence: 99%

Data Mining and Knowledge Discovery in Real Life Applications

Ponce¹,

Karahoca²

2009

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Knowledge Discovery and Data Mining are powerful data analysis tools. Data mining (DM) is an important tool for the mission critical applications to minimize, filter, extract or transform large databases or datasets into summarized information and exploring hidden patterns in knowledge discovery (KD). Sort of the data mining algorithms can be used in different real life applications. Classification, prediction, clustering, segmentation, summarization can be done on the large amount of data by using DM methods. The rapid dissemination of these technologies calls for an urgent examination of their social impact. The terms "Knowledge Discovery" and "Data Mining" are used to describe the 'non-trivial extraction of implicit, previously unknown and potentially useful information from data. Knowledge discovery is a concept that describes the process of searching on large volumes of data for patterns that can be considered knowledge about the data. The most well-known branch of knowledge discovery is data mining.Data mining is a multidisciplinary field with many techniques. With these techniques you can create a mining model that describe the data that you will use, some of these techniques are clustering algorithms, tools for data pre-processing, classification, regression, association rules, etc.. Data Mining is a fast-growing research area, in which connections among and interactions between individuals are analyzed to understand innovation, collective decision making, problem solving, and how the structure of organizations and social networks impacts these processes. Data Mining finds several applications; for instance, in different areas like biological, medicine, industrialist, business, economic, e-commerce, security, others.This book presents four different ways of theoretical and practical advances and applications of data mining in different promising areas like Industrialist, Biological, and Social. Twenty six chapters cover different special topics with proposed novel ideas. Each chapter gives an overview of the subjects and sort of the chapters have cases with offered data mining solutions. We hope that this book will be served as a Data Mining bible to show a right way for the students, researchers and practitioners in their studies. The twenty six chapters have been classified in four corresponding parts.• Knowledge Discovery • Clustering and Classification • Challenges and Benchmarks in Data Mining • Data Mining Applications The first part contains four chapters related to Knowledge Discovery. The focus of the contributions in this part, are the fundamental concepts and tools for extraction, representation, and retrieving of Knowledge in large Data Bases.The second part contains seven chapters related to Clustering and Classification. The focus of the contributions in this part, are the techniques and tools used to made clusters and classify the data on the basis of specific characteristics. VIIIThe third part contains three chapters related to Challenges and Benchmarks in Data Mining. The focus of the co...

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“…Association rules play also a role in traffic data analysis [65], [12]. A solution for mining a 3-year data set of traffic accidents based on association rules is proposed in [65].…”

Section: Association Rulesmentioning

confidence: 99%

“…Association rules play also a role in traffic data analysis [65], [12]. A solution for mining a 3-year data set of traffic accidents based on association rules is proposed in [65]. The data set contains 20 binary variables (referring to vehicles involved, the causes of the accident and human losses) and 3 nominal values (time of accident, scene and feature).…”

Section: Association Rulesmentioning

confidence: 99%

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

Yanes¹,

Enrique²

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According to recent studies, beyond being a major worldwide problem with huge economic impact, traffic collisions are poised to become as well one of the most important leading causes of death. Proactive traffic enforcement and intervention should be based on a thorough analysis on the collision data available to identify leading causes of accidents, the most prone locations as well as to predict the conditions for collision occurrence. This thesis presents a novel framework for collision prediction that takes into consideration historical and real-time factors, such as weather, geospatial information and social event data that can be obtained with existing sensor technology. A prototype is proposed, implemented and evaluated for the city of Ottawa, Canada, to predict: (1) accident frequency (collision vs nocollisions) and (2) accident severity (in terms of fatal, injury and property damage only accidents). The best performance was achieved in both cases using gradient boosted trees.iii

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Structure-Based Rule Selection Framework for Association Rule Mining of Traffic Accident Data

Cited by 10 publications

References 2 publications

A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

Data Mining and Knowledge Discovery in Real Life Applications

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

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