A Bivariate Bayesian Hierarchical Extreme Value Model for Traffic Conflict-based Crash Estimation

Publication date: Available online 22 January 2020Source: Analytic Methods in Accident ResearchAuthor(s): Lai Zheng, Tarek SayedAbstractThere are two main issues associated with traffic conflict-based crash estimation. First, there are several conflict indicators which were shown to inherently represent partial severity aspects of traffic events. Therefore, combining more than one conflict indicator can result in more comprehensive understanding on the underlying level of safety. Second, the conflict extremes characterized by the indicators, which are most related to crashes, are rare and heterogeneous in nature. These issues need to be properly addressed to enhance the crash estimation from traffic conflicts. To this end, this study develops a bivariate Bayesian hierarchal extreme value modeling method, which consists of a bivariate extreme value model that integrates different conflict indicators in a unified framework and a Bayesian hierarchical structure that combines traffic conflicts of different sites and accounts for heterogeneity in conflict extremes. Two model estimation methods are proposed. The first is a two-stage method that estimates marginal distributions of individual conflict indicators (i.e., univariate Bayesian hierarchical extreme value model) at first and then estimates the dependence of the two indicators after marginal transformation. The second is a one-stage estimation that combines the transformation and dependence parameter inference in a single st...
Source: Analytic Methods in Accident Research - Category: Accident Prevention Source Type: research