Investigating the Asymmetric Effects of Economic and Demographic Factors on Traffic Accident Severity – Evidence from a Nonlinear ARDL Approach

This study adopts an innovative nonlinear autoregressive distributed lag (N-ARDL) approach and compares it with the standard ARDL and the traditional ordinary least-squared (OLS) regressions. These approaches are used to examine the long-run and dynamic relationships between the accident severity in urban Jordan cities and the following explanatory variables: proportion of population living in urban areas, GDP growth rate, total length of road networks, vehicle ownership growth rate and spaces of newly added buildings. The case study covers the period from 2004 to 2022. The standard and nonlinear ARDL estimates showed the presence of long-run co-integration between variables. Moreover, the NARDL estimates on the long- and short-run indicate a varying effect of the considered explanatory variables on accident severity at the positive and negative partial sum. In general, GDP growth positively affects accident severity in the short run. Whereas, the expansion of road length presents a positive and negative impact on the upward and downward partial sum. In contrast, the explanatory variables differed in their impact on accident severity on each side of asymmetry. The variability of the results obtained by the nonlinear ARDL model helps suggest different policies for different high and low levels of traffic safety.