Recent Findings in Risk Management Described by Researchers from Ministry of Education (How to determine an optimal threshold to classify real-time…

Recent Findings in Risk Management Described by Researchers from Ministry of Education (How to determine an optimal threshold to classify real-time crash-prone traffic conditions?)

By a News Reporter-Staff News Editor at Insurance Weekly News -- A new study on Risk Management is now available. According to news originating from Shanghai, People's Republic of China, by VerticalNews correspondents, research stated, "One of the proactive approaches in reducing traffic crashes is to identify hazardous traffic conditions that may lead to a traffic crash, known as real-time crash prediction. Threshold selection is one of the essential steps of real-time crash prediction."

Funders for this research include National Science Foundation of China, 111 Project, Science and Technology Commission of Shanghai Municipality.

Our news journalists obtained a quote from the research from the Ministry of Education, "And it provides the cut-off point for the posterior probability which is used to separate potential crash warnings against normal traffic conditions, after the outcome of the probability of a crash occurring given a specific traffic condition on the basis of crash risk evaluation models. There is however a dearth of research that focuses on how to effectively determine an optimal threshold. And only when discussing the predictive performance of the models, a few studies utilized subjective methods to choose the threshold. The subjective methods cannot automatically identify the optimal thresholds in different traffic and weather conditions in real application. Thus, a theoretical method to select the threshold value is necessary for the sake of avoiding subjective judgments. The purpose of this study is to provide a theoretical method for automatically identifying the optimal threshold. Considering the random effects of variable factors across all roadway segments, the mixed logit model was utilized to develop the crash risk evaluation model and further evaluate the crash risk. Cross-entropy, between-class variance and other theories were employed and investigated to empirically identify the optimal threshold. And K-fold cross-validation was used to validate the performance of proposed threshold selection methods with the help of several evaluation criteria. The results indicate that (i) the mixed logit model can obtain a good performance; (ii) the classification performance of the threshold selected by the minimum cross-entropy method outperforms the other methods according to the criteria."

According to the news editors, the research concluded: "This method can be well-behaved to automatically identify thresholds in crash prediction, by minimizing the cross entropy between the original dataset with continuous probability of a crash occurring and the binarized dataset after using the thresholds to separate potential crash warnings against normal traffic conditions."

For more information on this research see: How to determine an optimal threshold to classify real-time crash-prone traffic conditions? Accident Analysis and Prevention, 2018;117():250-261. Accident Analysis and Prevention can be contacted at: Pergamon-Elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Accident Analysis and Prevention - www.journals.elsevier.com/accident-analysis-and-prevention/)

The news correspondents report that additional information may be obtained from R.J. Yu, Minist Educ, Key Lab Rd & Traff Engn, Shanghai 201804, People's Republic of China. Additional authors for this research include K. Yang, X.S. Wang, M. Quddus and L.F. Xue.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2018.04.022. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Keywords for this news article include: Shanghai, People's Republic of China, Asia, Risk Management, Ministry of Education.

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