Multilevel data and Bayesian analysis in traffic safety

    Authors

    H. Huang;M. Abdel-Aty

    Comments

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    Abbreviated Journal Title

    Accid. Anal. Prev.

    Keywords

    Road safety; Crash prediction models; Bayesian hierarchical models; Multilevel data; Spatiotemporal data; MOTOR-VEHICLE CRASHES; POISSON-GAMMA MODELS; SIGNALIZED INTERSECTIONS; ACCIDENT FREQUENCIES; RURAL INTERSECTIONS; REGRESSION-MODEL; GEOMETRIC; DESIGN; EMPIRICAL BAYES; SEVERITY; PREDICTION; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation

    Abstract

    Background: Traditional crash prediction models, such as generalized linear regression model, are incapable of taking into account multilevel data structure. Therefore they suffer from a common underlying limitation that each observation (e.g. a crash or a vehicle involvement) in the estimation procedure corresponds to an individual situation in which the residuals exhibit independence. Problem: However, this "independence" assumption may often not hold true since multilevel data structures exist extensively because of the traffic data collection and clustering process. Disregarding the possible within-group correlations may lead to production of models with unreliable parameter estimates and statistical inferences. Proposed theory: In this paper, a 5 x ST-level hierarchy is proposed to represent the general framework of multilevel data structures in traffic safety, i.e. [Geographic region level - Traffic site level - Traffic crash level - Driver-vehicle unit level - Occupant level] x Spatiotemporal level. The involvement and emphasis for different sub-groups of these levels depend on different research purposes and also rely on the heterogeneity examination on crash data employed. To properly accommodate the potential cross-group heterogeneity and spatiotemporal correlation due to the multilevel data structure, a Bayesian hierarchical approach that explicitly specifies multilevel structure and reliably yields parameter estimates is introduced and recommended. Case studies: Using Bayesian hierarchical models, the results from several case studies are highlighted to show the improvements on model fitting and predictive performance over traditional models by appropriately accounting for the multilevel data structure. (c) 2010 Elsevier Ltd. All rights reserved.

    Journal Title

    Accident Analysis and Prevention

    Volume

    42

    Issue/Number

    6

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    1556

    Last Page

    1565

    WOS Identifier

    WOS:000282240500006

    ISSN

    0001-4575

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