  In this paper, a new non-parametric empirical Bayes approach called CGAN-EB
is proposed for approximating empirical Bayes (EB) estimates in traffic
locations (e.g., road segments) which benefits from the modeling advantages of
deep neural networks, and its performance is compared in a simulation study
with the traditional approach based on negative binomial model (NB-EB). The
NB-EB uses negative binomial model in order to model the crash data and is the
most common approach in practice. To model the crash data in the proposed
CGAN-EB, conditional generative adversarial network is used, which is a
powerful deep neural network based method that can model any types of
distributions. A number of simulation experiments are designed and conducted to
evaluate the CGAN-EB performance in different conditions and compare it with
the NB-EB. The results show that CGAN-EB performs as well as NB-EB when
conditions favor the NB-EB model (i.e. data conform to the assumptions of the
NB model) and outperforms NB-EB in experiments reflecting conditions frequently
encountered in practice, specifically low sample means, and when crash
frequency does not follow a log-linear relationship with covariates.
