Predicting Pedestrian Involvement in Fatal Crashes Using a TabNet Deep Learning Model

To make road transportation systems safe for pedestrians, understanding the contributing features in vehicle-pedestrian fatal crashes is critical. With a better prediction model, it is possible to design effective countermeasures and reduce fatal crashes involving pedestrians. This paper aims to develop a deep learning-based model to predict fatal crashes that involve pedestrians in the United States using the Fatality Analysis Reporting System (FARS) database from the National Highway Traffic Safety Administration (NHTSA). TabNet architecture has been used to train a model from historical data. At the same time, other traditional classifiers such as support vector machines, random forests, and decision trees have been utilized to develop baseline results. An ensemble model of the five best models from the single model analysis was also developed. Metrics such as Precision, Recall, F1 score, and the area under the ROC curve (auROC) were calculated for each model. Since the problem requires correct prediction of all possible fatal cases, Recall is considered the most crucial evaluation metric. Not surprisingly, the ensemble model was found to have the highest recall value among all models. However, the TabNet model was found to have the highest recall score among single models, indicating that this model is the most suitable for the fatal vehicle-pedestrian crash prediction task out of all models analyzed. Another advantage of the TabNet model is that it can be interpreted, which helps understand the variables that contribute the most to the prediction. It was seen that factors such as roadway geometry, light conditions, impaired drivers, and land use had the highest contributions to the predictions made by the model. This fatal crash prediction model was found to have the potential to aid all relevant stakeholders in decision-making processes to make roadways safer.