Multivariate Crash Modeling For Motor Vehicle And Non-Motorized Modesat The Macroscopic Level

Keywords

Bayesian modeling; Macroscopic analysis; Multivariate modeling; Spatial modeling; Traffic analysis zones; Transportation safety planning

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.

Publication Date

1-1-2015

Publication Title

Accident Analysis and Prevention

Volume

78

Number of Pages

146-154

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2015.03.003

Socpus ID

84925012514 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84925012514

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