Researchers at University of Central Florida Release New Data on Risk Management (Real-time crash risk prediction on arterials based on LSTM-CNN)

2019 DEC 18 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Research findings on Risk Management are discussed in a new report. According to news reporting originating in Orlando, Florida, by NewsRx journalists, research stated, “Real-time crash risk prediction is expected to play a crucial role in preventing traffic accidents. However, most existing studies only focus on freeways rather than urban arterials.”

The news reporters obtained a quote from the research from the University of Central Florida, “This paper proposes a real-time crash risk prediction model on arterials using a long short-term memory convolutional neural network (LSTM-CNN). This model can explicitly learn from the various features, such as traffic flow characteristics, signal timing, and weather conditions. Specifically, LSTM captures the long-term dependency while CNN extracts the time-invariant features. The synthetic minority over-sampling technique (SMOTE) is used for resampling the training dataset. Five common models are developed to compare the results with the proposed model, such as the XGBoost, Bayesian Logistics Regression, LSTM, etc. Experiments suggest that the proposed model outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate.”

According to the news reporters, the research concluded: “The findings of this paper indicate the promising performance of using LSTM-CNN to predict real-time crash risk on arterials.”

For more information on this research see: Real-time crash risk prediction on arterials based on LSTM-CNN. Accident Analysis & Prevention, 2019;135():105371. 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 correspondents report that additional information may be obtained by contacting P. Li, Dept. of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, United States. Additional authors for this research include M. Abdel-Aty and J. Yuan.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2019.105371. 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.

The publisher of the journal Accident Analysis & Prevention can be contacted at: Pergamon-Elsevier Science LTD, the Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

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