An integrated solution to identify pedestrian-vehicle accident prone locations: GIS-based multicriteria decision approach

Abstract Spatial distributions of pedestrian-vehicle accident-prone locations (APLs) according to GIS-based models differ. Also, which APLs are determined by conventional models are more critical or which model is more successful in determining APL is still a major concern. To bridge this gap, this paper presents an innovative GIS-based Multi-Criteria Decision Making (MCDM) approach to identify the most critical APLs and to rank APLs with the compromising results of four GIS-based models. The results of planar KDE, network-based KDE, Getis-Ord Gi*, and Local Moran’s I which are weighted with prediction accuracy index (PAI), were evaluated together with MCDM methods: traditional VIKOR and psychometric VIKOR. Results & Discussion: The 15 most critical APLs in the compromise solution were ranked for four time periods. Network-based KDE gave the best performance, while Local Moran’s I performed the worst. Sensitivity analysis showed that the Psychometric VIKOR provides acceptable stability in the rankings of the APLs. The innovative MCDM approaches allowed the results of several models to be evaluated together. Thus, more reliable APLs were identified. Local governments with limited budgets can determine which APLs should be considered to improve pedestrian safety with the recommended approach and can apply to any study area.