Deep neural networks have been successfully used in many different areas of traffic engineering, such as crash prediction, intelligent signal optimization and real-time road surface condition monitoring. The benefits of deep neural networks are often uniquely suited to solve certain problems and can offer improvements in performance when compared to traditional methods. In collision prediction, uncertainty estimation is a critical area that can benefit from their application, and accurate information on the reliability of a model’s predictions can increase public confidence in those models. Applications of deep neural networks to this problem that consider these effects have not been studied previously. This paper develops a Bayesian deep neural network for crash prediction and examines the reliability of the model based on three key methods: layer-wise greedy unsupervised learning, Bayesian regularization and adapted marginalization. An uncertainty equation for the model is also proposed for this domain for the first time. To test the performance, eight years of car collision data collected from Highway 401, Canada, is used, and three experiments are designed.

Road Safety Analysis; Deep Learning; Uncertainty Analysis