A method to account for and estimate underreporting in crash frequency research. (October 2016)

• Record Type: Journal Article
• Authors: Wood, Jonathan S.; Donnell, Eric T.; Fariss, Christopher J.
• Is Part Of: Accident analysis and prevention. Volume 95:Part A(2016)
• Page Start: 57
• Page End: 66
• Publication Date: 2016-10
• Subjects: Crash underreporting -- Negative binomial underreporting -- Poisson underreporting with heterogeneity -- Random parameters negative binomial -- Crash frequency -- Predictive modeling; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2016.06.013
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: ⿢ Not accounting for crash underreporting may lead to biased estimates. ⿢ Underreporting models can be used to reduce bias from underreporting. ⿢ Underreporting model results are compared to random parameters negative binomial. ⿢ The comparisons indicated that the underreporting models yield better predictions. ⿢ Underreporting models produce reasonable estimates of crash underreporting. 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.