Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Applications of deep learning in traffic congestion detection,
  prediction and alleviation: A survey   Detecting, predicting, and alleviating traffic 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
for such 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
alleviation of congestion. Recurring and non-recurring 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.

27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th