Data mining of tree-based models to analyze freeway accident frequency.
BACKGROUND
Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems.


METHODS
This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors.


RESULTS
The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.


CONCLUSIONS
By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.
Chang
Li-Yen
LY
Graduate Institute of Transportation and Logistics, National Chia-Yi University, Taiwan. liyen@mail.ncyu.edu.tw
Chen
Wen-Chieh
WC
D000063:Accidents, Traffic / Q000706:statistics & numerical data* / Q000639:trends; D000465:Algorithms; D001185:Artificial Intelligence; D016010:Binomial Distribution; D016208:Databases, Factual; D005544:Forecasting / Q000379:methods; D006801:Humans; D016247:Information Storage and Retrieval; D015233:Models, Statistical; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D018570:Risk Assessment