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Journal Article

Citation

Eustace D, Alanazi F, Hovey PW. Adv. Transp. Stud. 2019; 47: 77-88.

Copyright

(Copyright © 2019, Arcane Publishers)

DOI

unavailable

PMID

unavailable

Abstract

The question of how vehicle color may contribute to motor vehicle crashes has weighed on the minds of consumers, safety advocates, and insurance companies for many years. This study uses a stratified induced exposure design where data are placed into two groups. The first group called the color prone crash group comprises the types of crashes where vehicle color visibility may have contributed in causing the crash such as when a vehicle is struck while in transport in multiple vehicle collisions. The second group is called an induced exposure crash group, which generally comprises crashes for which vehicle visibility was not likely a contributing factor in causing the crash, such as when a single vehicle crashes or when a vehicle strikes a parked vehicle or other fixed/stationery objects or simply overturns. Both the negative binomial (NB) and Poisson distributions were used to fit generalized linear models to the data. Model goodness-of-fit tests were utilized to check which model fits better to the data. Model goodness-of-fit tests indicate that the NB model reflected a better fit to the data due to over-dispersion.

RESULTS from the NB model confirm that statistically, besides random variations, no vehicle color was found to be safer or riskier than white, the vehicle color used as a baseline color.

Keywords: vehicle color; traffic, safety; induced exposure; visibility


Language: en

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