Investing in road safety enhancement programs highly depends on the economic valuation of road traffic accidents and their outcomes. Such evaluation underpins road safety interventions in cost-benefit analysis. To this end, understanding and modeling public willingness-to-pay for enhanced road safety have received significant attention in the past few decades. However, despite considerable modeling efforts, some issues still persist in earlier studies, namely, (i) using standard regression approaches that assume a homogeneous impact of explanatory variables on willingness-to-pay, not accounting for heterogeneity, and depends on a priori distribution of the dependent variable, and (ii) the absence of higher-order interactions from models, leading to omitted variable bias and erroneous model inferences. To overcome this critical research gap, our study proposes a new modeling framework, integrating a machine learning technique (decision tree) to identify a priori relationships for higher-order interactions and a quantile regression model to account for heterogeneity along the entire range of willingness-to-pay. The proposed framework examines the determinants of willingness-to-pay for enhanced road safety using a sample of car drivers from Peshawar, Pakistan. Modeling results indicate that variables not significant in a linear model become significant at specific quantiles of the willingness-to-pay distribution. Further, including higher-order interactions among the explanatory variables provides additional insights into the complex relationship between willingness-to-pay and its determinants. In addition, willingnessto-pay for fatal and severe injury risk reductions is estimated at different quartiles and used to calculate the values of corresponding risk reductions. Overall, the proposed framework provides a better understanding of public sensitivities to willingness-to-pay for enhanced road safety.
