| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 107 === The purpose of this study is to disclose variables that affect traffic accidents by examining a large amount of historical data. Since traditional statistical methods need a lot of pre-hypotheses and would lead to biased results, this study uses a non-parametric method for the analysis and proposes a severity prediction model. In the model, methods of Classification and Regression Tree (CART) and Random Forests are used. The latter can fix the over-fitting problem of the former. Synthetic Minority Over-sampling Technique (SMOTE) is also employed for solving the problem of label imbalance. For case study, traffic accident data of Taipei city from 2012 to 2017 are considered. The applicability of the proposed model is demonstrated through the case study. Moreover, it is found the most critical variable that causes traffic accidents of Taipei city during that period is “vehicle type.” About causality, motorcycles, pedestrians, and bicycles possess higher risks as compared to passenger cars and trucks.
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