A GAN-based framework for predicting pedestrian road safety based on land use and Points of Interest (POIs)

Pedestrian road safety represents a critical challenge in rapid urbanization processes, with land use and point of interest (POIs) configurations playing a central role in shaping crash risks. Existing studies mainly rely on traditional quantitative models, which often struggle to capture nonlinear spatial relationships and lack real-time, visual feedback for planning. This study develops a Generative Adversarial Network (GAN)-based framework to predict pedestrian crashes, integrating land use, POIs, and commuting distance to capture both direct and indirect associations. Using 504 spatial analysis units in Auckland, New Zealand, and 2870 pedestrian crash records (2016-2024), the model performs image-to-image translation to simulate crash risks under different urban configurations. Findings show that pedestrian crash risks vary nonlinearly across space and are linked to the combined effects of land use, POIs, and commuting distance. In the CBD, reducing commuting attraction areas emerges as a key factor associated with improved safety outcomes, with public open space expansion reducing pedestrian crashes by up to 14.7%. Sub-centre areas demonstrate sharp risk amplification (up to 80.4%) when educational, commercial, and transportation POIs simultaneously reach high densities. Residential areas exhibit significant threshold effects, where high-density combinations are associated with extreme risk scenarios (up to 35.5%). Moreover, direct associations significantly outweigh indirect pathways. This study offers a scenario-based visual tool for exploring the safety implications of objection and POI planning, providing empirical evidence for data-driven urban design.