Researchers from Texas A&M University Describe Findings in Risk Management (Patterns of Near-crash Events In a Naturalistic Driving Dataset: Applying Rules Mining): Risk Management

2021 NOV 11 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on Risk Management. According to news reporting originating from College Station, Texas, by NewsRx correspondents, research stated, “This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method - the Apriori algorithm. To provide more insights into near-crash behavior, this study classified near-crash events into two severity levels: trivial near-crash events (-7.5 g< deceleration rate < -4.5 g) and non-trivial near-crash events (<-7.5 g).”

Our news editors obtained a quote from the research from Texas A&M University, “From the perspective of descriptive statistics, the frequency of the itemsets, a set of categories of various variables, generated by the Apriori algorithm suggests that near-crash events are highly associated with several factors, including roadways without access control, driving during non-peak hours, roadways without a shoulder or a median, roadways with the minor arterial functional class, and roadways with a speed limit between 30 and 60 mph. By comparing the frequency of the occurrence of the itemset during trivial and non-trivial near-crash events, the results indicate that the length of the trip is a strong indicator of the near-crash event type. The results show that non-trivial near-crash events are more likely to occur if the trip is longer than 2 h. After applying the association rule mining algorithm, more interesting patterns for the two near-crash events were generated through the rules. The main findings include: 1) trivial nearcrash events are more likely to occur on roadways without a median and shoulder that have a relatively lower functional class; 2) relatively higher functional roadways with relatively wide medians and shoulders could be an intriguing combination for non-trivial near-crash events; 3) non-trivial near-crash events often occur on long trips (more than 2 h); 4) congestion on roadways that have a lower functional class is a dominant rule associating with the high frequency of non-trivial near-crash events.”

According to the news editors, the research concluded: “This study associates near-crash events and the corresponding road geometry and trip features to provide a unique understanding of near-crash events.”

This research has been peer-reviewed.

For more information on this research see: Patterns of Near-crash Events In a Naturalistic Driving Dataset: Applying Rules Mining. Accident Analysis & Prevention, 2021;161:106346. 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 Xiaoqiang Kong, Texas A&M University, 3136 Tamu, College Stn, TX 77843, United States. Additional authors for this research include Yunlong Zhang, Subasish Das and Hongmin Tracy Zhou.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2021.106346. 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.

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