Findings from Ministry of Education Provides New Data on Risk Management (Applicability of Boosting Techniques In Calibrating Safety Performance Functions for Freeways): Risk Management

2021 OCT 04 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators discuss new findings in Risk Management. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “Safety performance functions (SPFs) are indispensable analytical tools that usually play a crucial role in estimating crash frequencies, identifying hotspots, analyzing crash contributing factors, and assessing the effectiveness of safety countermeasures. Due to the limited availability of safety data, municipalities tended to adopt SPFs from Highway Safety Manual or other neighboring jurisdictions.”

Financial support for this research came from International Science and Technology Cooperation Programme.

Our news journalists obtained a quote from the research from the Ministry of Education, “Recently, boosting algorithms have been frequently exploited for data analysis and statistical regression modeling problems. This research, therefore, aims to examine the efficiency of boosting calibration techniques to transfer the SPF using the limited region data in an international context. To this end, AdaBoost.R2, an adaptive boosting algorithm, Two-stage TrAdaBoost.R2, an instance-based transfer learning algorithm, and Gradient Boosting algorithm were employed to investigate their efficiencies in acquiring knowledge from the available source domain data to predict crashes in the target domain. As a comparison, the calibration factor method was adopted to transfer the traditional negative binomial (NB) regression model. Two training dataset groups were developed to train the four calibration techniques. The first group was used to examine the adaptability of the employed calibration techniques to the limited target region data. While the second group was utilized to further investigate the influence of larger vital information on the performance of transferred models. This study was conducted between two U.S. states, Florida and New York, and two Chinese cities, Shanghai and Suzhou. According to the goodness-of-fit results, boosting calibration techniques showed better prediction accuracy than the calibrated NB-based model using the limited target region data.”

According to the news editors, the research concluded: “In addition, the amount and distribution of the training dataset were considered the two significant factors that influence the proficiency of the boosting calibration techniques.”

This research has been peer-reviewed.

For more information on this research see: Applicability of Boosting Techniques In Calibrating Safety Performance Functions for Freeways. Accident Analysis & Prevention, 2021;159:106193. 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 Xuesong Wang, Ministry of Education, Key Lab Rd & Traff Engn, Shanghai, People’s Republic of China. Additional authors for this research include Ahmad Yehia, Mingjie Feng, Xiaohan Yang, Jian Gong and Zhixing Zhu.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2021.106193. 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.

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