Probabilistic Linkage Approach to Commercial Motor Vehicle and Carrier Datasets

In this paper, a probabilistic linkage method is explored in the context of linking databases in the Commercial Motor Vehicle and Carrier (CMVC) sector as a potential solution to overcome data quality problems. An application of this method is demonstrated by linking commercial motor vehicle inspection files kept by the Illinois State Police (ISP) and the inspection files available from the Illinois portion of the Motor Carrier Management Information System (MCMIS). Since one of the files to be matched is a subset of the other, the application allows us to validate the methodology. The results show 6,228 correct identifications of true matched record pairs out of 6,335 actual true matches (more than 99%) between the two files. The number of erroneously identified record pairs is 690 (about 11% of the actual true matched pairs.) Sensitivity analysis is conducted of error rates with respect to variations in the optimal thresholds for merging the databases. A simple analysis also shows how much of a clerical examination for unclear record pairs would have to be tolerated for a reduction in dollar expenditure.


Issue Date:
2004
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/206730
Published in:
Journal of the Transportation Research Forum, Volume 43, Number 2
Page range:
37-52
Total Pages:
17




 Record created 2017-04-01, last modified 2017-08-28

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