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Crash prediction is a critical component of road safety analyses. A widelyadopted approach to crash prediction is application of regression basedtechniques. The underlying calibration process is often time-consuming,requiring significant domain knowledge and expertise and cannot be easilyautomated. This paper introduces a new machine learning (ML) based approach asan alternative to the traditional techniques. The proposed ML model is calledregularized deep belief network, which is a deep neural network with twotraining steps: it is first trained using an unsupervised learning algorithmand then fine-tuned by initializing a Bayesian neural network with the trainedweights from the first step. The resulting model is expected to have improvedprediction power and reduced need for the time-consuming human intervention. Inthis paper, we attempt to demonstrate the potential of this new model for crashprediction through two case studies including a collision data set from 800 kmstretch of Highway 401 and other highways in Ontario, Canada. Our intention isto show the performance of this ML approach in comparison to varioustraditional models including negative binomial (NB) model, kernel regression(KR), and Bayesian neural network (Bayesian NN). We also attempt to addressother related issues such as effect of training data size and trainingparameters.

An Improved Deep Belief Network Model for Road Safety Analyses