National Taiwan University Reports Findings in Risk Management (Temporal stability of associations between crash characteristics: A multiple correspondence analysis): Risk Management

2022 FEB 25 (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 originating from Taipei, Taiwan, by NewsRx correspondents, research stated, “Understanding the associations between crash characteristics facilitates the development of traffic safety policies for improving traffic safety. This study investigates the temporal stability of associations between crash characteristics at different temporal levels using multiple correspondence analysis (MCA).”

Our news journalists obtained a quote from the research from National Taiwan University, “For each date in 2020, crash data from the previous week, month, season, half year, one year, two years, three years, and four years are collected respectively as eight temporal levels. MCA plots and chi-square distance analysis are used to assess the temporal stability of associations between crash characteristics across dates in 2020 with data from various temporal levels. The key findings of this study demonstrate that associations between crash characteristics at lower temporal levels show notable and potential cyclical variations across dates, while more stable and long-term trend of associations between crash characteristics may be identified as the temporal level increases, especially at the two-year level and higher temporal levels at which temporal stability may be expected. The study contributes to the literature by presenting a challenge for traffic analysts in that both temporally stable and unstable associations between crash characteristics may be observed at any point in time when different temporal levels are considered as study periods.”

According to the news editors, the research concluded: “Therefore, it may serve as a foundation for future research and practical works to identify traffic safety issues and optimal policies as well as facilitate the interpretation of statistical modeling in the presence of temporally unstable data.”

This research has been peer-reviewed.

For more information on this research see: Temporal stability of associations between crash characteristics: A multiple correspondence analysis. Accident Analysis & Prevention, 2022;168:106590. 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 correspondents report that additional information may be obtained from Tien-Pen Hsu, Dept. of Civil Engineering, National Taiwan University, Taipei 106, Taiwan. Additional authors for this research include Yuan-Wei Wu and Albert Y. Chen.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2022.106590. 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’s 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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