| dc.description.abstract | The substantial decline in motor-vehicle fatal crashes over the period of 2008 to 2011 has been subjected to extensive research in the last few years. Starting from the early 1970s, reduced fatalities have been associated with economic downfalls by looking into empirical historical evidence in many countries of the world. Following the perceptible reduction in fatalities in the United States (U.S.) beginning in 2008, which concurred with a major economic recession during the same period, some researchers focused on finding the relative influence of such a hypothesis using statistical modeling. This study sought to serve as an extension of the Project 17-67 by the National Cooperative Highway Research Program (NCHRP) to provide a thorough investigation of the factors influencing fatalities during and after the 2008 recession using an updated dataset. Two Poisson-gamma regression models, considering (MCS) or not considering (MNCS) a varying effect among states, and a log-change regression model were developed under the Project NCHRP 17-67. The primary research objectives were to run the existing models with a state-based dataset from 2001 to 2012 and recalibrate it with an updated dataset to 2016 to check the adequacy of the models in predicting fatalities after the recession or if any additional variable is required. The study further investigated the inconsistent effect of the recession on fatalities by land use type. The modeling results showed remarkable improvements with the updated dataset, where both the MNCS and MCS models could reflect the fluctuations in fatalities over the focus period. The Poisson-gamma models outperformed the log-change model in predicting total, rural, and urban fatalities. The effect analysis revealed that the economic factors contribute as much as 84% to 86% in the reduction and subsequent increase in fatalities during and after the recession. The unemployment rate of 16 to 24 years old, median household income, and the price of gasoline were found to be the most statistically significant parameters in both the models. The goal of this research was to provide a better understanding of the economic variables affecting fatalities, alongside focus on rarely addressed issues like variable effect on rural versus urban environments. | en |
