Integrating Perceived Risk and Spatial Data for Urban Junction Safety: A Micro-Spatial Machine Learning Approach
DOI:
https://doi.org/10.59796/jcst.V16N2.2026.185Keywords:
crash risk perception, ensemble machine learning, geographic information systems, near-miss identification, road safety, spatial analysis, urban mobilityAbstract
Road traffic crashes (RTCs) remain a leading global cause of mortality, yet conventional safety analyses often overlook how individuals perceive road risk, a key behavioral dimension influencing crash likelihood. In many developing cities, the absence of comprehensive crash databases further limits evidence-based safety planning. This study addresses these challenges by developing a transferable ensemble machine learning framework across junctions that predicts crash-risk hotspots using perceived risk as a proxy under data-scarce conditions. The framework integrates GIS (Geographic Information Systems)-based infrastructure mapping, KDE (Kernel Density Estimation)-derived spatial perception maps, PROMETHEE-based parameter prioritization, and an ensemble of Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT) to enable scalable, microspatial safety analysis. Perceptions of risk across behavioral, infrastructure, and environmental factors were systematically mapped using Geographic Information Systems (GIS) and modeled through the ensemble model. The optimized ensemble achieved high predictive performance (R² = 0.92, kappa = 0.779), effectively integrating psychological perception with spatial infrastructure data at a micro-spatial resolution of 1 m × 1 m. Strong correlations emerged between perceived risk and determinants such as tailgating tendency (r = 0.9347), building density and land-use mix (r = 0.9497), and poor lighting in dense areas (r = –0.3714). The resulting Crash Risk Perception Index (CRPI) revealed distinct spatial clusters, distinguishing validated danger zones from perceptual–empirical divergences. Hidden hazard zones (6.1% of cells) - high crash frequency but low perceived risk - and near-miss zones (12.7% of cells) - low crash frequency but high perceived risk - were identified as priority locations for targeted awareness, enforcement, and design interventions. Validation across three additional urban junctions (kappa = 0.72–0.83) demonstrated robustness and transferability. The framework enables cities lacking crash databases to proactively identify high-risk and near-miss zones, supporting perception-informed and evidence-based strategies for safer, more resilient urban mobility systems.
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