A method to account for and estimate underreporting in crash frequency research

Jonathan S. Wood, Eric T. Donnell, Christopher J. Fariss

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Underreporting is a well-known issue in crash frequency research. However, statistical methods that can account for underreporting have received little attention in the published literature. This paper compares results from underreporting models to models that account for unobserved heterogeneity. The difference in the elasticities between the negative binomial underreporting model and random parameters negative binomial models, which accounts for unobserved heterogeneity in crash frequency models, are used as the basis for comparison. The paper also includes a comparison of the predicted number of unreported PDO crashes based on the negative binomial underreporting model with crashes that were reported to police but were not considered reportable to PennDOT to assess the ability of the underreporting models to predict non-reportable crashes. The data used in this study included 21,340 segments of two-lane rural highways that are owned and maintained by PennDOT. Reported accident frequencies over an eight year period (2005–2012) were included in the sample, producing a total of 170,468 segment-years of data. The results indicate that if a variable impacts both the true accident frequency and the probability of accidents being reported, statistical modeling methods that ignore underreporting produce biased regression coefficients. The magnitude of the bias in the present study (based on elasticities) ranged from 0.00–16.79%. If the variable affects the true accident frequency, but not the probability of accidents being reported, the results from the negative binomial underreporting models are consistent with analysis methods that do not account for underreporting.

Original languageEnglish (US)
Pages (from-to)57-66
Number of pages10
JournalAccident Analysis and Prevention
Volume95
DOIs
StatePublished - Oct 1 2016

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health

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