Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

    Authors

    R. J. Yu; M. Abdel-Aty;M. Ahmed

    Comments

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    Abbreviated Journal Title

    Accid. Anal. Prev.

    Keywords

    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

    Abstract

    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.

    Journal Title

    Accident Analysis and Prevention

    Volume

    50

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    371

    Last Page

    376

    WOS Identifier

    WOS:000314191600043

    ISSN

    0001-4575

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