Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level

    Authors

    J. Lee; M. Abdel-Aty;X. M. Jiang

    Comments

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    Abbreviated Journal Title

    Accid. Anal. Prev.

    Keywords

    Multivariate modeling; Macroscopic analysis; Bayesian modeling; Spatial; modeling; Traffic analysis zones; Transportation safety planning; ACCIDENT PREDICTION MODELS; TRAFFIC ANALYSIS ZONES; STATISTICAL-ANALYSIS; SPATIAL-ANALYSIS; LAND-USE; SAFETY; SEVERITY; FREQUENCIES; INFRASTRUCTURE; HETEROGENEITY; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation

    Abstract

    Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode. (C) 2015 Elsevier Ltd. All rights reserved.

    Journal Title

    Accident Analysis and Prevention

    Volume

    78

    Publication Date

    1-1-2015

    Document Type

    Article

    Language

    English

    First Page

    146

    Last Page

    154

    WOS Identifier

    WOS:000353600000018

    ISSN

    0001-4575

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