Crash Severity Prediction Using Two-Layer Ensemble Machine Learning Model for Proactive Emergency Management

Many unfortunate victims in road traffic crashes do not receive ideal treatment because their injury severity is not understood at an early stage. Swift crash severity prediction enables trauma and emergency centers to estimate the potential damage resulting from a road traffic crash and accordingly dispatch the proper emergency units to provide appropriate emergency treatment. A two-layer ensemble machine learning model is proposed in this study to predict road traffic crash severity. The first layer integrates four base machine learning models: k-nearest neighbor, decision tree, adaptive boosting, and support vector machine; the second layer classifies the crash severity based on the feedforward neural network model. The models are developed using road traffic crash data of road intersections over 6 years (2011-2016) obtained from Great Britain's Department of Transport online database. Only the crash features that can be instantaneously and easily obtained are used as an input. To simplify the two-layer ensemble model, principal component analysis technique is used for dimensionality reduction in the second layer of the model. The performance of the two-layer ensemble model is compared with five base models: k-nearest neighbor, decision tree, adaptive boosting, support vector machine, and feedforward neural network. The prediction results reveal that the two-layer ensemble model outperforms the five base classification models based on two performance indicators: testing accuracy and F1 score. The transferability of the developed model is tested using the 3-year crash dataset for Canada obtained from the National Crash Database Online. The outcome indicates that the two-layer ensemble model shows the best performance for the Canadian dataset also. The proposed two-layer ensemble model would be beneficial in predicting crash severity with high accuracy based on limited initial crash information obtained from the crash location. Using this information, trauma centers would be able to prepare for appropriate and prompt medical treatment.