Integrated Accident Resilience Framework (IARF) – A Theoretical Approach Using Spatial and Statistical Analysis

Abstract

Throughout the world, road accidents have become a nightmare for any local government. Data shows that every 24 seconds someone dies on the road (WHO, 2018). Generally, there are multiple factors causing road accidents such as traffic volumes/composition, speed, infrastructure conditions, climatic conditions, and vehicle factors etc. Through this paper, an effort has been made to bring an effective Integrated Accident Resilience Framework (IARF). The framework is in the form of a theoretical method which may help transportation agencies and governments to develop a practical system for crash analysis and mitigation. The Integrated Accident Resilience Framework (IARF) showcased in this paper consists of different stages such as data collection, storage, and analysis, which help to compute correlations between crash causational parameters and crash frequency. The tools used to perform the analysis functions in the framework consist of the GIS platform, as well as the application of the negative binomial regression model. The computed results help identify the major influencing parameters that are linked to traffic accidents and their contribution to crash frequency in black spot locations. This can be used to mitigate future crashes by taking appropriate remedial measures in collision-prone regions. The methodology presented can also be scaled up to a city level network. The entire transportation network can be spatially marked to develop a resilient accident management strategy; even a real-time also.