Traffic accident data are usually noisy, contain missing values, and
heterogeneous. How to select the most important variables to improve real-time
traffic accident risk prediction has become a concern of many recent studies.
This paper proposes a novel variable selection method based on the Frequent
Pattern tree (FP tree) algorithm. First, all the frequent patterns in the
traffic accident dataset are discovered. Then for each frequent pattern, a new
criterion, called the Relative Object Purity Ratio (ROPR) which we proposed, is
calculated. This ROPR is added to the importance score of the variables that
differentiate one frequent pattern from the others. To test the proposed
method, a dataset was compiled from the traffic accidents records detected by
only one detector on interstate highway I-64 in Virginia in 2005. This dataset
was then linked to other variables such as real-time traffic information and
weather conditions. Both the proposed method based on the FP tree algorithm, as
well as the widely utilized, random forest method, were then used to identify
the important variables or the Virginia dataset. The results indicate that
there are some differences between the variables deemed important by the FP
tree and those selected by the random forest method. Following this, two
baseline models (i.e. a nearest neighbor (k-NN) method and a Bayesian network)
were developed to predict accident risk based on the variables identified by
both the FP tree method and the random forest method. The results show that the
models based on the variable selection using the FP tree performed better than
those based on the random forest method for several versions of the k-NN and
Bayesian network models.The best results were derived from a Bayesian network
model using variables from FP tree. That model could predict 61.11% of
accidents accurately while having a false alarm rate of 38.16%.

本文提出了一种基于 FP 树算法的变量选择方法用于实时预测交通事故风险，并利用基于这种方法和随机森林方法选择的变量建立了 k-NN 方法和贝叶斯网络模型，结果表明基于 FP 树的变量选择方法可以更好地预测车祸风险，其中使用 FP 树方法建立的贝叶斯网络模型预测正确率为 61.11％，假警报率为 38.16％。