A Data Coding And Screening System For Accident Risk Patterns: A Learning System
Free (open access)
505 - 516
F. Geçer Sargın, Y. Duvarcı, E. İnan, B. Kumova & İ. Atay Kaya
Accidents on urban roads can occur for many reasons, and the contributing factors together pose some complexity in the analysis of the casualties. In order to simplify the analysis and track changes from one accident to another for comparability, an authentic data coding and category analysis methods are developed, leading to data mining rules. To deal with a huge number of parameters, first, most qualitative data are converted into categorical codes (alpha-numeric), so that computing capacity would also be increased. Second, the whole data entry per accident are turned into ID codes, meaning each crash is possibly unique in attributes, called ‘accident combination’, reducing the large number of similar value accident records into smaller sets of data. This genetical code technique allows us to learn accident types with its solid attributes. The learning (output averages) provides a decision support mechanism for taking necessary cautions for similar combinations. The results can be analyzed by inputs, outputs (attributes), time (years) and the space (streets). According to Izmir’s case results; sampled data and its accident combinations are obtained for 3 years (2005 - 2007) and their attributes are learned. Keywords: traffic accidents, data mining, similarity index, learning systems.
traffic accidents, data mining, similarity index, learning systems