Abstract
Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.
Highlights
According to the statistics of the United States, the impact of incidents accounts for 50~60 percent of total delay on US freeways [1]
Based on Classification and Regression Tree (CART) method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors
The prediction performance provided by CART method and Negative Binomial (NB) regression model with training and testing data was considered to be similar, and it can be seen that CART analysis is an effective method to forecast incident frequency
Summary
According to the statistics of the United States, the impact of incidents accounts for 50~60 percent of total delay on US freeways [1]. Evaluating and analyzing the traffic delay caused by incidents has become more and more significant during the last decades. Most of regression models require the assumptions among the variables, and if these assumptions are violated, or homoscedasticity of the residuals is violated, the erroneous analysis results would be generated [2], e.g. the assumptions of the linear regression model requires that the dependent variable is continuous, the relationship between variables is inherently linear, and the observations are independently and randomly sampled. When any of the requirements are not met, the analysis results may be biased, and remedial actions should be taken
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