Texas A&M University Reports Findings in Risk Management (Derivation of the Empirical Bayesian method for the Negative Binomial-Lindley generalized linear model with application in traffic safety): Risk Management

2022 APR 11 (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 reporting from College Station, Texas, by NewsRx journalists, research stated, “The expected crash frequency is the long-term average crash count for a specific site. It is extensively used to systematically evaluate the crash risk associated with roadway elements.”

The news correspondents obtained a quote from the research from Texas A&M University, “To estimate the expected crashes, the Empirical Bayesian (EB) approach is typically employed. The EB method is a computationally convenient approximation to the Full Bayesian (FB) method, which gained popularity due to its simple interpretation, computational efficiency, and the ability to account for the regression to the mean bias. However, the common EB method used in traffic safety analysis is only applicable when the traditional Negative Binomial (NB) model is used. The NB model, however, is not appropriate when data is highly dispersed, skewed, or has a large number of zero observations. The Negative Binomial-Lindley (NB-L) model is a mixture of the NB and Lindley distributions and has shown superior fit compared to the NB model, especially when the dataset is characterized by excess zero observations. Even though several studies have used the NB-L in developing crash prediction models, the application of the NB-L in other safety-related tasks (e.g., hot spot identification) is largely neglected. This study proposed a framework to develop the EB method for the NB-L model and subsequently estimate the expected crash values.A comparison between the EB and FB estimates was performed to validate the approximation framework in general. The results indicated that the proposed EB framework is able to estimate expected crashes with comparable precision to the FB estimate, but with much less computational cost. In addition, a site ranking analysis using the EB estimates was conducted to validate the proposed approximation method in safety studies. However, it should be noted that any other type of safety analysis that requires access to the expected crashes can benefit from the proposed EB method. This study concluded that the proposed EB framework can properly approximate the underlying FB approach and can reasonably be considered as an alternative to the traditional EB formula derived from the NB model.”

According to the news reporters, the research concluded: “The results of this study can help to extend the application of the advanced predictive models beyond predicting crashes to other safety-related tasks, with no additional computational efforts.”

This research has been peer-reviewed.

For more information on this research see: Derivation of the Empirical Bayesian method for the Negative Binomial-Lindley generalized linear model with application in traffic safety. Accident Analysis & Prevention, 2022;170:106638. Accident Analysis & Prevention can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

Our news journalists report that additional information may be obtained by contacting Ali Khodadadi, Texas A&M University, 3136 TAMU, College Station, TX 77843-3136, United States. Additional authors for this research include Ioannis Tsapakis, Mohammadali Shirazi, Subasish Das and Dominique Lord.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2022.106638. 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.

Publisher 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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