  Crash prediction is a critical component of road safety analyses. A widely
adopted approach to crash prediction is application of regression based
techniques. The underlying calibration process is often time-consuming,
requiring significant domain knowledge and expertise and cannot be easily
automated. This paper introduces a new machine learning (ML) based approach as
an alternative to the traditional techniques. The proposed ML model is called
regularized deep belief network, which is a deep neural network with two
training steps: it is first trained using an unsupervised learning algorithm
and then fine-tuned by initializing a Bayesian neural network with the trained
weights from the first step. The resulting model is expected to have improved
prediction power and reduced need for the time-consuming human intervention. In
this paper, we attempt to demonstrate the potential of this new model for crash
prediction through two case studies including a collision data set from 800 km
stretch of Highway 401 and other highways in Ontario, Canada. Our intention is
to show the performance of this ML approach in comparison to various
traditional models including negative binomial (NB) model, kernel regression
(KR), and Bayesian neural network (Bayesian NN). We also attempt to address
other related issues such as effect of training data size and training
parameters.
