Hierarchical spatial network models for road accident risk assessment

Abstract This paper addresses the critical issue of road safety and accident prevention by integrating road features, network theory, and advanced statistical models. It emphasises the importance of understanding the relationship between road infrastructure and accident risk, which impacts on various administrative stakeholders and on citizens’ safety. While existing literature focuses on road features and engineering solutions, this paper highlights the need to consider implicit spatial constraints as well. Our study builds on prior research by proposing a novel approach that merges conditional autoregressive modelling with a two-stage mixed Geographically weighted Poisson regression. This integrated methodology allows us to consider both the effect of risk factors at a global level and at a local road level. By leveraging the strengths of these two methods, we aim to capture both overarching trends and local variations of risk factors, thereby offering a comprehensive understanding of accident risk factors. Using data from the Open Street Map database, which covers the wide province of Milan in Italy, our models identify influential street characteristics, providing valuable insights for informed decision-making regarding road safety measures. Our method can be applied to any region in the world. The paper describes the models used, the dataset employed, and presents a detailed numerical analysis demonstrating the effectiveness of the approach in identifying and understanding accident risk factors within road networks. This information can help guide investments for the benefit of society.