title: Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level.
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.
authors: Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States. Electronic address: jaeyoung@knights.ucf.edu.
authors: Jaeyoung
authors: J
authors: Lee
authors: Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States.
authors: Mohamed
authors: M
authors: Abdel-Aty
authors: Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States.
authors: Ximiao
authors: X
authors: Jiang
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D001642:Bicycling / Q000706:statistics & numerical data*; D005431:Florida / Q000453:epidemiology; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D016012:Poisson Distribution; D012449:Safety / Q000706:statistics & numerical data*; D016138:Walking / Q000706:statistics & numerical data*; D014947:Wounds and Injuries / Q000453:epidemiology*; D055815:Young Adult
keywords: Bayesian modeling; Macroscopic analysis; Multivariate modeling; Spatial modeling; Traffic analysis zones; Transportation safety planning