A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction. (June 2015)

• Record Type: Journal Article
• Authors: Lin, Lei; Wang, Qian; Sadek, Adel W.
• Is Part Of: Transportation research. Volume 55(2015)
• Page Start: 444
• Page End: 459
• Publication Date: 2015-06
• Subjects: Frequent Pattern tree (FP tree) -- Fuzzy C-means clustering (FCM) -- Bayesian network -- k Nearest Neighbor (k-NN) -- Variable importance -- Variable selection -- Random forest -- Real time -- Relative Object Purity Ratio (ROPR) -- Traffic accident risk prediction; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2015.03.015
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We propose a novel variable selection method based on Frequent Pattern tree. A variable importance metric, the Relative Object Purity Ratio, is introduced. k nearest neighbor and Bayesian networks for accident risk prediction are developed. The proposed variable selection method is compared to the random forest algorithm. Abstract: With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k -nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k -nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.