  Intelligent Transportation Systems (ITS) are much correlated with data
science mechanisms. Among the different correlation branches, this paper
focuses on the neural network learning models. Some of the considered models
are shallow and they get some user-defined features and learn the relationship,
while deep models extract the necessary features before learning by themselves.
Both of these paradigms are utilized in the recent intelligent transportation
systems (ITS) to support decision-making by the aid of different operations
such as frequent patterns mining, regression, clustering, and classification.
When these learners cannot generalize the results and just memorize the
training samples, they fail to support the necessities. In these cases, the
testing error is bigger than the training error. This phenomenon is addressed
as overfitting in the literature. Because, this issue decreases the reliability
of learning systems, in ITS applications, we cannot use such over-fitted
machine learning models for different tasks such as traffic prediction, the
signal controlling, safety applications, emergency responses, mode detection,
driving evaluation, etc. Besides, deep learning models use a great number of
hyper-parameters, the overfitting in deep models is more attention. To solve
this problem, the regularized learning models can be followed. The aim of this
paper is to review the approaches presented to regularize the overfitting in
different categories of ITS studies. Then, we give a case study on driving
safety that uses a regularized version of the convolutional neural network
(CNN).
