@article{ref1,
title="Applying Bayesian data mining to measure the effect of vehicular defects on crash severity",
journal="Journal of transportation safety and security",
year="2021",
author="Das, Subasish and Dutta, Anandi and Geedipally, Srinivas Reddy",
volume="13",
number="6",
pages="605-621",
abstract="The National Motor Vehicle Crash Causation Survey (NMVCCS), conducted from 2005 to 2007, showed that an estimated 44,000 crashes occurred due to vehicular defects-- 2% of the NMVCCS crashes. Vehicle defects have an adverse effect upon overall roadway safety as they can increase the likelihood of traffic crashes, thus increasing the frequency of crash-related injuries and fatalities. Even though Louisiana requires a biennial vehicular safety inspection, recent traffic crash statistics have shown a higher than average percentage of vehicle defect-related crash fatalities in Louisiana (3% of all traffic fatalities). This fact called for an in-depth analysis of the vehicle defect-related crashes in Louisiana. The current study used 7 years (2010-2016) of traffic crash data from Louisiana to investigate the association between crash severity and vehicle-defect types by applying a Bayesian data mining approach. The findings showed that vehicle age is associated with severe injury crashes. Worn tires and defective brakes are the over-represented vehicle-defect categories. The significant association patterns can be used by different stakeholders to enhance roadway safety and reduce vehicular defect associated crashes.<p /> <p>Language: en</p>",
language="en",
issn="1943-9962",
doi="10.1080/19439962.2019.1658674",
url="http://dx.doi.org/10.1080/19439962.2019.1658674"
}