Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors
Abbreviated Journal Title
Accid. Anal. Prev.
Mountainous freeway safety; Bayesian inference; Real-time weather data; and random effect model; FREQUENCY; INTERSECTIONS; SEVERITY; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation
Freeway crash occurrences are highly influenced by geometric characteristics, traffic status, weather conditions and drivers' behavior. For a mountainous freeway which suffers from adverse weather conditions, it is critical to incorporate real-time weather information and traffic data in the crash frequency study. In this paper, a Bayesian inference method was employed to model one year's crash data on 1-70 in the state of Colorado. Real-time weather and traffic variables, along with geometric characteristics variables were evaluated in the models. Two scenarios were considered in this study, one seasonal and one crash type based case. For the methodology part, the Poisson model and two random effect models with a Bayesian inference method were employed and compared in this study. Deviance Information Criterion (DIC) was utilized as a comparison factor. The correlated random effect models outperformed the others. The results indicate that the weather condition variables, especially precipitation, play a key role in the crash occurrence models. The conclusions imply that different active traffic management strategies should be designed based on seasons, and single-vehicle crashes have different crash mechanism compared to multi-vehicle crashes. (C) 2012 Elsevier Ltd. All rights reserved.
Accident Analysis and Prevention
"Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors" (2013). Faculty Bibliography 2010s. 4904.