Bangladesh and other developing nations face road traffic accidents (RTAs) as a major public safety issue. Accidents have grown due to growing motor vehicle use, obsolete infrastructure, and lax traffic law enforcement. The Khulna–Rajshahi Highway experiences frequent and severe road crashes, resulting in high fatalities, injuries, and economic losses. The study aims to identify crash hotspots, assess land-use influences on road safety across varying road environments, and propose infrastructure and policy measures to enhance safety along the Khulna–Rajshahi corridor, with particular emphasis on VRU risks due to their disproportionate burden in Bangladesh's mixed-traffic environment. The research relied on a combination of Geographic Information System (GIS) tools, police-reported accident data from 2023 to 2024 integrated with VRU survey data (n = 208), and high-resolution satellite imagery. Advanced spatial techniques such as Kernel Density Estimation (KDE), Getis-Ord Gi∗ hotspot analysis, and Geographically Weighted Regression (GWR) were used to uncover meaningful patterns, yielding a model fit of R 2 = 0.27. The results indicate a notable clustering of accidents (Moran's I = 0.065, p < 0.05), with urban stretches along the corridor reporting the highest concentrations, reaching up to 30–35 accidents per square kilometer. Severity mapping revealed critical danger zones in places like Puthia (Rajshahi), Boraigram Upazila (Natore), among others. Due to poor infrastructure and traffic management, motorcyclists (77% of VRUs) and pedestrians were especially susceptible in mixed-use zones near lively markets and junctions, where approximately 45% of vulnerable road user (VRU) incidents occurred. This study suggests immediate, focused safety measures to decrease accidents. Building pedestrian overpasses and placing traffic lights in high-risk areas are key. A replicable GIS-based, corridor-level evidence framework that uniquely integrates VRU behavioral data with spatial statistics allows planners and transport authorities to prioritize location-specific interventions to reduce road traffic accidents in developing-country highway contexts. • The study focuses on reducing traffic accidents, improving public safety, and providing policy recommendations. • Severity Index, ANN, Cluster Analysis, KDE, Gi-statistics, and other methods were used. • A Moran's I value of 0.064 indicates that accidents are clustered. • The study suggests adjusting existing policies and introducing new ones.
Jany et al. (Sun,) studied this question.