  Prediction tasks related to congestion are targeted at improving the level of
service of the transportation network. With increasing access to larger
datasets of higher resolution, the relevance of deep learning in such
prediction tasks, is increasing. Several comprehensive survey papers in recent
years have summarised the deep learning applications in the transportation
domain. However, the system dynamics of the transportation network vary greatly
between the non-congested state and the congested state -- thereby
necessitating the need for a clear understanding of the challenges specific to
congestion prediction. In this survey, we present the current state of deep
learning applications in the tasks related to detection, prediction and
propagation of congestion. Recurrent and non-recurrent congestion are discussed
separately. Our survey leads us to uncover inherent challenges and gaps in the
current state of research. Finally, we present some suggestions for future
research directions as answers to the identified challenges.
