A hybrid optimization framework for road traffic accident data

With the exponential growth of the number of vehicles in the traffic, road traffic accidents have become a fast-growing and wide-spreading menace, causing the loss of precious human life and economic assets. In addition to the fast growth of population and motorization, roads are highly occupied by cars, buses, trucks, motorcycles, minibikes, taxis, pedestrians, animals, and other travelers. The main challenges in the road traffic accident data prediction and analysis is the small size of the dataset that can be used for training. While road traffic accident causes millions of deaths and injuries every year, their density in time and space is –fortunately-low. This makes machine learning very challenging at the local level. The main target of this work is to minimize the Causality Severity of road traffic accidents. Mathematical analysis for the attributes’ effect on Causality Severity was guided to select effective twenty decision variables. An optimization search requires a reliable simulated model that depicts the necessary characteristics of the system under study. The good formulation of the simulated model was reduced the non-linearity and interaction between the variables. The simulated model has been verified by successive linear technique with aid of the Successive Linear Programming (SLP). Mixed Integer nonlinear Program (MINLP) has proven a reliable optimization search with the present model. Reasonable agreements have found when compared the simulated solutions with the experimental data. From the output results, we observed that some of the attributes have a positive effect on Causality-Severity, while other attributes have a negative effect on it. In addition to the implementation output of both MINLP and SLP, we can state that the Age of Driver, Speed Limit, and Age of Causality are necessary and sensitive variables for objective Causality Severity. Optimal results would guide for selecting the best conditions.