Tsinghua University Reports Findings in Risk Management (Analysis and prediction of intersection traffic violations using automated enforcement system data): Risk Management

2021 OCT 20 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Risk Management is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “The automated enforcement system (AES) is an effective way of supplementing traditional traffic enforcement, and the traffic violation data from AES can also be effectively used for safety research. In this study, traffic violation data were used to analyze the influencing factors associated with traffic violations and to predict the probability of violations at intersections.”

Financial supporters for this research include National Key Research and Development Program of China Stem Cell and Translational Research, National Key Research and Development Program of China.

Our news journalists obtained a quote from the research from Tsinghua University, “The potential factors influencing violations include 24 independent factors related to time, space, traffic and weather. Results from a logistic model showed that the midday period, weekends, residential districts, collector roads, congested traffic conditions, high traffic flow, lower wind speed and low temperature would increase the probability of traffic violations. The probability of violations was predicted by the random forest algorithm, which was proven to be the best traffic violation prediction model among logistic regression, Gaussian naive Bayes, and support vector machine. Moreover, the proximity weighted synthetic oversampling technique (ProWSyn) method was applied to reduce the impact of the imbalance ratio (IR) and improve the model’s prediction performance. The receiver operating characteristics (ROC) curves and Precision-Recall (PR) curves illustrated that the random forest algorithm using oversampling data had the best classifier prediction performance than undersampling data.”

According to the news editors, the research concluded: “The area under curve (AUC) and out-of-bag (OOB) error with IR = 1 reached 0.914 and 0.0787, which showed the better performance of the random forest algorithm using ProWSyn in dealing with imbalanced traffic violation data.”

This research has been peer-reviewed.

For more information on this research see: Analysis and prediction of intersection traffic violations using automated enforcement system data. Accident Analysis & Prevention, 2021;162:106422. Accident Analysis & Prevention can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Accident Analysis & Prevention - http://www.journals.elsevier.com/accident-analysis-and-prevention/)

Our news journalists report that additional information may be obtained by contacting Meng Li, Dept. of Civil Engineering, Tsinghua University, Beijing 100084, People’s Republic of China. Additional authors for this research include Yunxuan Li, Jinghui Yuan, Jian Lu and Mohamed Abdel-Aty.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2021.106422. 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.

Publisher contact information for the journal Accident Analysis & Prevention is: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

(Our reports deliver fact-based news of research and discoveries from around the world.)