Loughborough University Reports Findings in Risk Management (The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections)

2021 FEB 04 (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 Loughborough, United Kingdom, by NewsRx correspondents, research stated, “With the emergence of connected vehicle (CV) technology, there is a doubt whether CVs can improve driver intentions and behaviors, and thus protect them from accidents with the provision of real-time information. In order to understand the possible impacts of the real-time information provided by CV technology on drivers, this paper aims to develop a model which considers the heterogeneity between drivers with the aid of the extended theory of planned behavior.”

Funders for this research include National Natural Science Foundation of China, National Key Research and Development Plan, Joint Research Scheme of National Natural Science Foundation of China/Research Grants Council of Hong Kong, Postgraduate Research and Innovation Project of Central South University.

Our news journalists obtained a quote from the research from Loughborough University, “At the uncontrolled non-signalized intersections, a stated preference (SP) questionnaire survey was conducted to build the dataset consisting of 1001 drivers. Based on the collected dataset, the proposed model examines the relationships between subjective norms, attitudes, risk perceptions, perceived behavioral control and driving intentions, and studies how such driving intentions are simultaneously related to driver characteristics and experiences in the CV environment. Furthermore, driver groups which are homogenous with respect to personality traits are formed, and then are employed to analyze the heterogeneity in responses to driving intentions. Four key findings are obtained when analyzing driver responses to the real-time information provided by CV technology: 1) the proposed H-ETPB model is verified with a good fitness measure; 2) irrespective to driver personality traits, attitudes and perceived behavioral control have a direct and indirect association with driving intentions to accelerate; 3) driving intentions of high-neurotic drivers to accelerate are significantly related to subjective norms, while that of low-neurotic drivers are not; 4) elder high-neurotic drivers, and low-neurotic drivers who have unstable salaries or ever joined in online car hailing service have a strong intention in accelerating.”

According to the news editors, the research concluded: “The findings of this study could provide the theoretical framework to optimize traffic performance and information design, as well as provide in-vehicle personalized information service in the CV and CAV environments and assist traffic authorities to design the most acceptable traffic rules for different drivers at an uncontrolled non-signalized intersection.”

This research has been peer-reviewed.

For more information on this research see: The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections. Accident Analysis & Prevention, 2021;151:105934. Accident Analysis & Prevention can be contacted at: Elsevier, 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 Mohammed Quddus, School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, LE113TU, UK. Additional authors for this research include Wenjing Zhao, Helai Huang, Qianshan Jiang, Kui Yang and Zhongxiang Feng.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2020.105934. 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: Elsevier, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

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