New Risk Management Study Results from National Taiwan University Described (Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data)

2020 DEC 22 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Current study results on Risk Management have been published. According to news reporting originating from Taipei, Taiwan, by NewsRx correspondents, research stated, “Traffic violations and improper driving are behaviors that primarily contribute to traffic crashes. This study aimed to develop effective approaches for predicting at-fault crash driver frequency using only city-level traffic enforcement predictors.”

Financial support for this research came from Ministry of Science and Technology and National Taiwan University (NTU), Taiwan.

Our news editors obtained a quote from the research from National Taiwan University, “A fusion deep learning approach combining a convolution neural network (CNN) and gated recurrent units (GRU) was developed to compare predictive performance with one econometric approach, two machine learning approaches, and another deep learning approach. The performance comparison was conducted for (1) at-fault crash driver frequency prediction tasks and (2) city-level crash risk prediction tasks. The proposed CNN-GRU achieved remarkable prediction accuracy and outperformed other approaches, while the other approaches also exhibited excellent performances. The results suggest that effective prediction approaches and appropriate traffic safety measures can be developed by considering both crash frequency and crash risk prediction tasks. In addition, the accumulated local effects (ALE) plot was utilized to investigate the contribution of each traffic enforcement activity on traffic safety in a scenario of multicollinearity among predictors. The ALE plot illustrated a complex nonlinear relationship between traffic enforcement predictors and the response variable.”

According to the news editors, the research concluded: “These findings can facilitate the development of traffic safety measures and serve as a good foundation for further investigations and utilization of traffic violation data.”

This research has been peer-reviewed.

For more information on this research see: Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data. Accident Analysis & Prevention, 2020;150:105910. 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/)

The news editors report that additional information may be obtained by contacting Yuan-Wei Wu, Dept. of Civil Engineering, National Taiwan University, Taipei, 106, Taiwan.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2020.105910. 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.

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