International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 85 - 97

A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning

Authors
Shengdong Du1, Tianrui Li1, *, Xun Gong1, Shi-Jinn Horng2
1School of Information Science and Technology, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China
2Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
*Corresponding author. Email: trli@swjtu.edu.cn
Corresponding Author
Tianrui Li
Received 17 March 2019, Accepted 15 January 2020, Available Online 23 January 2020.
DOI
10.2991/ijcis.d.200120.001How to use a DOI?
Keywords
Traffic flow forecasting; Multimodal deep learning; Gated recurrent units; Attention mechanism; Convolutional neural networks
Abstract

Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which can jointly and adaptively learn the spatial–temporal correlation features and long temporal interdependence of multi-modality traffic data by an attention auxiliary multimodal deep learning architecture. According to the highly nonlinear characteristics of multi-modality traffic data, the base module of our method consists of one-dimensional convolutional neural networks (1D CNN) and gated recurrent units (GRU) with the attention mechanism. The former is to capture the local trend features and the latter is to capture the long temporal dependencies. Then, we design a hybrid multimodal deep learning framework for fusing share representation features of different modality traffic data by multiple CNN-GRU-Attention modules. The experimental results indicate that the proposed multimodal deep learning model is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
85 - 97
Publication Date
2020/01/23
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200120.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Shengdong Du
AU  - Tianrui Li
AU  - Xun Gong
AU  - Shi-Jinn Horng
PY  - 2020
DA  - 2020/01/23
TI  - A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
JO  - International Journal of Computational Intelligence Systems
SP  - 85
EP  - 97
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200120.001
DO  - 10.2991/ijcis.d.200120.001
ID  - Du2020
ER  -