A Feature Importance Analyzable Resilient Deep Neural Network for Road Safety Performance Function Surrogate Modeling

The safety performance function (SPF) is an extensively employed tool in road safety assessment. However, traditional modeling methods often fall short of effectively capturing the intricate interdependencies among diverse traffic variables. To address this limitation, a feature importance analyzable resilient deep neural network (RDNN) is proposed as an alternative approach. This model begins with an explainable autoencoder that delineates the relationship between observed collisions and road characteristics. Subsequently, it introduces <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> unsupervised feature importance analysis process that enriches the original input data. The enhanced input is then processed by a novel RDNN, featuring an automated Gaussian transfer function and resilient supervised learning, both meticulously designed for precise modeling. Ultimately, the efficacy of the proposed framework is demonstrated through several case studies on real-world applications, utilizing data collected from highways in Canada and the U.S.