Analysis of traffic accident severity using Decision Rules via Decision Trees
[EN] A Decision Tree (DT) is a potential method for studying traffic accident severity. One of its main advantages is that Decision Rules can be extracted from its structure and used to identify safety problems and establish certain measures of performance. However, when it used only one DT, the rul...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/102304 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/102304 |
| Access Level: | acceso abierto |
| Palabra clave: | Traffic accident Severity Road safety Decision Trees Decision Rules INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES |
| Sumario: | [EN] A Decision Tree (DT) is a potential method for studying traffic accident severity. One of its main advantages is that Decision Rules can be extracted from its structure and used to identify safety problems and establish certain measures of performance. However, when it used only one DT, the rule extraction is limited to the structure of that DT and some important relationships between variables cannot be extracted. This paper presents a method for extracting rules from a DT more effectively. The method¿s effectiveness when applied to a particular traffic accidents dataset is shown. Specifically, our study focuses on traffic accident data from rural roads in Granada (Spain) from 2003 to 2009 (both included). The results show that we can obtain more than 70 relevant rules from our data using the new method, whereas with only one DT we would had extracted only 5 rules from the same dataset. |
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