Integrating Pattern Features to Sequence Model for Traffic Index Prediction

Authors

Yueying Zhang^{1, *,} , Zhijie Xu¹, Jianqin Zhang², Jingjing Wang³, Lizeng Mao³

¹School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China

²School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

³Beijing Municipal Transportation Operation Coordination Center, Beijing, 102616, China

^*Corresponding authors. Email: 1061180208@qq.com

DOI

10.2991/ijcis.d.210510.001How to use a DOI?

Keywords

Deep learning; Traffic index prediction; Pattern features learning; Sequence-to-sequence network

Abstract

Intelligent traffic system (ITS) is one of the effective ways to solve the problem of traffic congestion. As an important part of ITS, traffic index prediction is the key of traffic guidance and traffic control. In this paper, we propose a method integrating pattern feature to sequence model for traffic index prediction. First, the pattern feature of traffic indices is extracted using convolutional neural network (CNN). Then, the extracted pattern feature, as auxiliary information, is added to the sequence-to-sequence (Seq2Seq) network to assist traffic index prediction. Furthermore, noticing that the prediction curve is less smooth than the ground truth curve, we also add a linear regression (LR) module to the architecture to make the prediction curve smoother. The experiments comparing with long short-term memory (LSTM) and Seq2Seq network demonstrated advantages and effectiveness of our methods.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

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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TY  - JOUR
AU  - Yueying Zhang
AU  - Zhijie Xu
AU  - Jianqin Zhang
AU  - Jingjing Wang
AU  - Lizeng Mao
PY  - 2021
DA  - 2021/05/14
TI  - Integrating Pattern Features to Sequence Model for Traffic Index Prediction
JO  - International Journal of Computational Intelligence Systems
SP  - 1589
EP  - 1596
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210510.001
DO  - 10.2991/ijcis.d.210510.001
ID  - Zhang2021
ER  -