Quantifying the Risk Impact of Contextual Factors on Pedestrian Crash Outcomes in Data-Scarce Developing Country Settings

Pedestrian crashes remain a leading cause of road traffic fatalities in developing coun-tries (DCs), yet reliable crash data are scarce, limiting the calibration of global safety models such as the International Road Assessment Programme (iRAP) to local contexts. This study presents a methodological framework for quantifying the influence of con-textual risk factors on pedestrian crash frequency in data-scarce environments. Artificial datasets comprising 2000 random samples per variable were generated from litera-ture-derived distributions representing DC conditions. Analytical procedures, includ-ing pairwise correlation, stepwise regression, and Negative Binomial (NB) modelling, were applied to estimate Factor Influence values (Fi) and identify variables absent from the iRAP model. Six NB models were developed; none of the 20 modelled variables met conventional statistical significance thresholds, underscoring that the results are illus-trative rather than inferential. Comparative analysis revealed 16 factors absent from iRAP, including “countermeasure as an afterthought” (Fi = 0.63), and 5 factors neither modelled nor covered by iRAP. This approach demonstrates a replicable process for prioritising safety factors in DC contexts, to be calibrated with real-world data in future studies.