Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multistep traffic speed prediction: A deep learning based approach using
  latent space mapping considering spatio-temporal dependencies   Traffic management in a city has become a major problem due to the increasing
number of vehicles on roads. Intelligent Transportation System (ITS) can help
the city traffic managers to tackle the problem by providing accurate traffic
forecasts. For this, ITS requires a reliable traffic prediction algorithm that
can provide accurate traffic prediction at multiple time steps based on past
and current traffic data. In recent years, a number of different methods for
traffic prediction have been proposed which have proved their effectiveness in
terms of accuracy. However, most of these methods have either considered
spatial information or temporal information only and overlooked the effect of
other. In this paper, to address the above problem a deep learning based
approach has been developed using both the spatial and temporal dependencies.
To consider spatio-temporal dependencies, nearby road sensors at a particular
instant are selected based on the attributes like traffic similarity and
distance. Two pre-trained deep auto-encoders were cross-connected using the
concept of latent space mapping and the resultant model was trained using the
traffic data from the selected nearby sensors as input. The proposed deep
learning based approach was trained using the real-world traffic data collected
from loop detector sensors installed on different highways of Los Angeles and
Bay Area. The traffic data is freely available from the web portal of the
California Department of Transportation Performance Measurement System (PeMS).
The effectiveness of the proposed approach was verified by comparing it with a
number of machine/deep learning approaches. It has been found that the proposed
approach provides accurate traffic prediction results even for 60-min ahead
prediction with least error than other techniques.

18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg