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

Accident; Analysis and Prevention
Jaeyoung LeeXimiao Jiang

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.

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Citations

May 23, 2016·Accident; Analysis and Prevention·Richard Amoh-GyimahMajid Sarvi
Jul 22, 2016·Accident; Analysis and Prevention·Shamsunnahar Yasmin, Naveen Eluru
Aug 1, 2016·Accident; Analysis and Prevention·Xuesong WangShikai You
Nov 16, 2016·Accident; Analysis and Prevention·Kai WangEric Jackson
Dec 20, 2019·Accident; Analysis and Prevention·Mohammad Mehdi BesharatiCarlo G Prato
Apr 30, 2021·Accident; Analysis and Prevention·Shamsunnahar YasminNaveen Eluru
Jun 16, 2021·International Journal of Injury Control and Safety Promotion·Wen ChengAaron Truong
Apr 8, 2019·Accident; Analysis and Prevention·N N SzeLu Bai

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