Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Capsule Network for Traffic Speed Prediction in Complex Road Networks   This paper proposes a deep learning approach for traffic flow prediction in
complex road networks. Traffic flow data from induction loop sensors are
essentially a time series, which is also spatially related to traffic in
different road segments. The spatio-temporal traffic data can be converted into
an image where the traffic data are expressed in a 3D space with respect to
space and time axes. Although convolutional neural networks (CNNs) have been
showing surprising performance in understanding images, they have a major
drawback. In the max pooling operation, CNNs are losing important information
by locally taking the highest activation values. The inter-relationship in
traffic data measured by sparsely located sensors in different time intervals
should not be neglected in order to obtain accurate predictions. Thus, we
propose a neural network with capsules that replaces max pooling by dynamic
routing. This is the first approach that employs the capsule network on a time
series forecasting problem, to our best knowledge. Moreover, an experiment on
real traffic speed data measured in the Santander city of Spain demonstrates
the proposed method outperforms the state-of-the-art method based on a CNN by
13.1% in terms of root mean squared error.

34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th