Abstract
In recent years, many researchers have studied the risk models, which are able to prevent various accidents occurred on riding mountain bike. The bikers prevent themselves from the accidents using a model, which is able to predict the risk of mountain bike. The objective of this paper is to build a method of risk model for predicting mountain bike accident using information of mountain bike courses obtained by global positioning system and difficulty of courses for bikers. We evaluate the classification methods such as C5.0, Bayesian network, neural network, and support vector machine to find a method with higher accuracy comparing with other. Also, we build a risk model using factors with high risk on riding mountain bike. Proposed method ensures better selection of a course and reduction of the accident risk for mountain bikers.
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Seo, DH. et al. (2011). Assessment Building a Method for Risk Model of Mountain Bike Accident Based on Classification Techniques. In: Lee, G., Howard, D., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2011. Lecture Notes in Computer Science, vol 6935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24082-9_87
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DOI: https://doi.org/10.1007/978-3-642-24082-9_87
Publisher Name: Springer, Berlin, Heidelberg
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