TY  - JOUR
PY  - 2021//
TI  - Applying Bayesian data mining to measure the effect of vehicular defects on crash severity
JO  - Journal of transportation safety and security
A1  - Das, Subasish
A1  - Dutta, Anandi
A1  - Geedipally, Srinivas Reddy
SP  - 605
EP  - 621
VL  - 13
IS  - 6
N2  - 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>
LA  - en
SN  - 1943-9962
UR  - http://dx.doi.org/10.1080/19439962.2019.1658674
ID  - ref1
ER  -