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 countries (DCs); yet reliable crash data are scarce, constraining the ability to model pedestrian safety risks and evaluate countermeasure effectiveness. This study developed a methodological process for estimating the influence of contextual factors on pedestrian crashes using artificial data. The process integrated literature-derived trend analysis, artificial data generation, external face validity checks, correlation analysis, stepwise negative binomial regression, sensitivity testing, and mapping of results against the International Road Assessment Programme (iRAP) framework. Of the 26 contextual factors considered, 20 were retained in the negative binomial (NB) models, while six were excluded due to weak or inconsistent trend data. Results showed that behavioural and institutional factors, including ad hoc countermeasure implementation, gender composition of pedestrian flows, and vehicle age or technology, exerted stronger influence on crash outcomes than several geometric variables typically emphasised in global models. External validity testing confirmed broad alignment of the artificial dataset with published values, while sensitivity analysis demonstrated the robustness of factor influence values (Fi) across bootstrap resampling and scenario perturbations. The Fi values derived are illustrative rather than decision-ready, reflecting the artificial-data basis of this study. Nonetheless, the findings highlight methodological proof of concept that artificial-data modelling can provide credible and context-sensitive insights in data-scarce environments. Mapping results to the iRAP framework revealed complementarity, with opportunities to extend global models by incorporating behavioural and institutional variables more systematically. The approach provides a replicable pathway for improving pedestrian safety assessment in DCs and informs the development of an enhanced iRAP effectiveness model in subsequent research. Future applications should prioritise empirical calibration with real-world crash datasets and support policymakers in integrating behavioural and institutional factors into countermeasure prioritisation and safety planning.