Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for
  Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact   Traffic speed prediction is a critically important component of intelligent
transportation systems (ITS). Recently, with the rapid development of deep
learning and transportation data science, a growing body of new traffic speed
prediction models have been designed, which achieved high accuracy and
large-scale prediction. However, existing studies have two major limitations.
First, they predict aggregated traffic speed rather than lane-level traffic
speed; second, most studies ignore the impact of other traffic flow parameters
in speed prediction. To address these issues, we propose a two-stream
multi-channel convolutional neural network (TM-CNN) model for multi-lane
traffic speed prediction considering traffic volume impact. In this model, we
first introduce a new data conversion method that converts raw traffic speed
data and volume data into spatial-temporal multi-channel matrices. Then we
carefully design a two-stream deep neural network to effectively learn the
features and correlations between individual lanes, in the spatial-temporal
dimensions, and between speed and volume. Accordingly, a new loss function that
considers the volume impact in speed prediction is developed. A case study
using one-year data validates the TM-CNN model and demonstrates its
superiority. This paper contributes to two research areas: (1) traffic speed
prediction, and (2) multi-lane traffic flow study.

27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
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
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an