Analysis of Factors Influencing Traffic Accidents in Sidoarjo Regency Using the Geographically Weighted Regression Method - Aprilianti, Inggrit Delima; Ulinnuha, Nurissaidah; Intan, Putroue Keumala (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung, 2025)
Abstract. Traffic accidents are incidents that may result in trauma, injury, disability, or even death. One of the regencies in East Java Province experiencing an annual increase in traffic accident cases is Sidoarjo Regency. Geographically Weighted Regression (GWR) is a statistical approach that analyses the relationship between independent and dependent variables, taking into account spatial variation in each region. This study applies the GWR method to identify significant factors influencing the number of traffic accidents and to classify sub-regions within Sidoarjo Regency based on those factors. This study uses variables such as accident count, population density, vehicle types, gender ratio, and geographic coordinates to capture spatial differences across Sidoarjo's districts. The results indicate that the adaptive tricube kernel in GWR is the most suitable model, achieving a coefficient of determination (R²) of 99.96%. This performance indicates that the GWR model yields a slightly better fit than the multiple linear regression model, which obtained an R² of 99.86%. The types of vehicles, specifically trucks, cars, and motorcycles, are identified as significant variables in almost all districts. In Sidoarjo Regency, the districts are classified into two clusters based on the independent variables that significantly influence traffic accidents: Cluster 1, the density–vehicle accident cluster, and Cluster 2, the vehicle-only accident cluster. This classification provides a foundation for more targeted government interventions to reduce regional traffic accidents. Policy recommendations include controlling population density and improving road infrastructure in the first cluster, while focusing on vehicle safety, monitoring goods transportation, and implementing road safety campaigns in the second cluster.