CiteSeerX — Mining Traffic Accident Features by Evolutionary Fuzzy Rules

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Mining Traffic Accident Features by Evolutionary Fuzzy Rules

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BibTeX

@MISC{_miningtraffic,
    author = {},
    title = {Mining Traffic Accident Features by Evolutionary Fuzzy Rules},
    year = {}
}

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Abstract

Abstract—Traffic accidents represent a major problem threat-ening peoples lives, health, and property. Traffic behavior and driving in particular is a social and cultural phenomenon that exhibits significant differences across countries and regions. Therefore, traffic models developed in one country might not be suitable for other countries. Similarly, attributes of importance, dependencies, and patterns found in data describing traffic in one region might not be valid for other regions. All this makes traffic accident analysis and modelling a task suitable for data mining and machine learning approaches that develop models based on actual real-world data. In this study, we investigate a data set describing traffic accidents in Ethiopia and use a machine learning method based on artificial evolution and fuzzy systems to mine symbolic description of selected features of the data set. Keywords—machine learning; genetic programming; fuzzy rules; traffic accidents; binary classification; multi-class classifi-cation I.

Keyphrases

traffic accident feature evolutionary fuzzy rule traffic accident data set significant difference cultural phenomenon traffic accident analysis multi-class classifi-cation data mining fuzzy rule binary classification genetic programming fuzzy system abstract traffic accident symbolic description traffic model artificial evolution actual real-world data major problem threat-ening people machine learning approach keywords machine learning traffic behavior