Contextualising Pedestrian Countermeasure Effectiveness in Developing Countries: A Variant iRAP Model with Weighted Risk Adjustment

Pedestrian crashes are disproportionately concentrated in developing countries (DCs), where scarce and unreliable crash data limit the calibration of global road safety as-sessment tools. The International Road Assessment Programme (iRAP) provides a globally recognised framework for evaluating pedestrian facilities; however, its coun-termeasure effectiveness (CME) values are derived largely from data from high-income countries. This study develops and tests a variant countermeasure effectiveness model that integrates locally relevant contextual factors not considered in iRAP. Building on a systematic review (which identified 33 contextual factors) and statistical modelling with artificial datasets (which profiled 16 absent factors), the study intro-duced two novel contributions: the Adjusted Effectiveness Formula (Eqn 1) and the Adjusted Pedestrian Star Rating Score (PSRS) Formula (Eqn 7). Eleven contextual factors were retained for integration based on a threshold criterion (Fi < 0.9 or Fi > 1.1). Four weighting methods (correlation, regression, principal component analysis, and budget allocation) were compared; the Budget Allocation Method was selected for its suitability in data-poor contexts. Interpolated risk values were generated across performance lev-els using a log-linear approach (Eqn 6). Application of the adjusted model revealed significant shifts in star rating outcomes. At operating speeds of 40–50 km/h, factors such as overtaking tendency, traffic rule enforcement, countermeasure as an afterthought, human capacity of agencies, and employed population caused several road segments to shift from Star 2 to Star 1, classifying them as unsafe at speeds above 45 km/h. This outcome, not captured by the current iRAP model, underscores the need for localised recalibration. The adjusted model, embedded directly into iRAP’s PSRS framework, improves realism and predictive accuracy, offering a practical tool for context-sensitive prioritisation of pedestrian safety countermeasures in DCs.