A full Bayesian random parameters Negative Binomial-Lindley model for fatal pedestrian crash frequency on rural highways.

Pedestrian safety on rural highways remains a pressing concern in low- and middle-income countries (LMIC), where highways frequently pass through settlements with substantial pedestrian activity but limited dedicated pedestrian infrastructures and prevalence of high-speed motorized traffic. This study investigates fatal pedestrian crash frequency on such roads using a fine-resolution segment-level approach. Five-year fatal pedestrian crash data (2017-2022, excluding 2020), comprising 337 pedestrian-involved crashes across six rural highway corridors, were analyzed by dividing the entire road network into 2,881 equal-length segments. Crash data were integrated with detailed segment-level information on traffic exposure, vehicular speeds, roadway geometry, roadside environment, landuse, and population exposure. Four count data models were estimated within a Full Bayesian framework to account for overdispersion, excess zeros, and unobserved heterogeneity, and the best-fitting model was identified for pedestrian crash frequency. Model performance was evaluated using the Deviance Information Criterion, predictive accuracies, and cumulative residual plots, which support the superiority of the Random Parameter Negative Binomial-Lindley (RPNB-L) model over its counterparts. Results indicate that the presence of junctions, settlements, landuse, flyover transition zones, canals/bridges/culverts, median gaps, service roads, minor accesses, and pedestrian activity generators such as bus stops, schools, fuel stations, and roadside eateries primarily drives pedestrian crash risk on rural highways. Population exposure emerges as a robust predictor of risk, while observed pedestrian volumes exhibit a safety-in-numbers effect. The findings emphasize the need for segment-level pedestrian safety strategies on high-speed rural highways and provide empirical evidence to support targeted infrastructure design, access management, and roadside environment interventions in LMICs.