Hefei University of Technology Reports Findings in Risk Management (Spatiotemporal analysis of crash severity on rural highway: A case study in Anhui, China): Risk Management

2021 DEC 30 (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 Anhui, People’s Republic of China, by NewsRx correspondents, research stated, “Traffic crashes are the result of the interaction between human activities and different socio-economic, geographical, and environmental factors, showing a temporal and spatial relationship. The temporal and spatial correlations must be characterized in crash severity studies, for which the geographically and temporally weighted ordered logistic regression (GTWOLR) model is an effective approach.”

Financial support for this research came from National Natural Science Foundation of China.

Our news journalists obtained a quote from the research from the Hefei University of Technology, “However, existing studies using the GTWOLR model only subjectively selected a type of kernel function and kernel bandwidth, which cannot determine the best expression of the spatiotemporal relationship between crashes. This paper explores the optimal kernel function and kernel bandwidth considering the aforementioned problem to obtain the best GTWOLR model to analyze the crash data based on the crash data of rural highways in Anhui Province, China, from 2014 to 2017. First, the GTWOLR models with Gaussian or Bi-square kernel function and fixed (the spatiotemporal distance remains constant of local sample) or adaptive (the quantity of the local sample is constant) bandwidth are compared. Second, the log-likelihood and Akaike information criterion are used to compare the GTWOLR model with the ordered logistic regression (OLR) model. Finally, the spatial and temporal characteristics of the contributing factors in the best GTWOLR model are analyzed, and corresponding countermeasures for improving traffic safety on rural highways are proposed. Model comparison results reveal that although the difference was insignificant, the Bi-square kernel function with fixed bandwidth (BF)- GTWOLR model has a better goodness of fit than the GTWOLR models with other types of kernel function and bandwidth and the OLR model. The BF-GTWOLR model estimation results showed that eight factors, including pedestrian-vehicle crash, middle-aged driver, hit-and-run, truck, motorcycle, curve, slope and mountainous, passed the non-stationary test, indicating their varying effects on the crash severity across space and over time. As a crash severity modeling approach that effectively quantifies the spatiotemporal relationships in crashes, the BF-GTWOLR model, which adapts to crash data, may have implications for future research.”

According to the news editors, the research concluded: “In addition, the findings of this paper can help traffic management departments to propose progressive and targeted policies or countermeasures, so as to reduce the severity of rural highway crashes.”

This research has been peer-reviewed.

For more information on this research see: Spatiotemporal analysis of crash severity on rural highway: A case study in Anhui, China. Accident Analysis & Prevention, 2021;165:106538. 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 Yikai Chen, School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, People’s Republic of China. Additional authors for this research include Renjia Luo, Mark King, Qin Shi, Jie He and Zongpin Hu.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2021.106538. 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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