Empirical Bayes Approach for Estimating Urban Deer-Vehicle Crashes Using Police and Maintenance Records

Abstract

Deer-vehicle crashes are a growing problem in Iowa where deer-vehicle crashes represent 13% of all crashes reported. In 2009, these crashes resulted in nine fatalities and 451 injuries in the state. Deer-vehicle crashes are a problem even in urban areas of Iowa. It is known that deer-vehicle crashes are typically underreported. To address this underreporting, deer carcass salvage reports may be used to augment deer-vehicle crash reports. The objective of this paper is to exploit two sources of deer-vehicle crash data using a statistically reliable assessment methodology, empirical Bayes, to assess the potential for safety improvement of 150 urban highway sections in Iowa. Reconciliation of records to reduce double counting is discussed and a negative binomial regression model of deer-vehicle crash frequency as a function of roadway and environmental factors is estimated. The 25 most promising segments for deer-vehicle crash countermeasures are identified, mostly located on high-speed roadways, roadway segments with gravel right shoulders, and segments adjacent to grasslands. The methodology facilitates the identification of locations for countermeasure implementation as well as monitoring deer-vehicle crash trends.