Mining traffic accident features by evolutionary fuzzy rules
Mining traffic accident features by evolutionary fuzzy rules
- New search for: Kromer, Pavel
- New search for: Beshah, Tibebe
- New search for: Ejigu, Dejene
- New search for: Snasel, Vaclav
- New search for: Platos, Jan
- New search for: Abraham, Ajith
- New search for: Kromer, Pavel
- New search for: Beshah, Tibebe
- New search for: Ejigu, Dejene
- New search for: Snasel, Vaclav
- New search for: Platos, Jan
- New search for: Abraham, Ajith
Traffic accidents represent a major problem threatening 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.
Document information
Mining traffic accident features by evolutionary fuzzy rules
2013-04-01
172252 byte
Conference paper
Electronic Resource
English
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