Urban Traffic Flow Fore-casting based on Deep Learning Model

DOI

10.2991/icmeit-19.2019.113How to use a DOI?

Keywords

Deep learning; Traffic flow forecasting; Neural network; Machine learning.

Abstract

Short-term traffic flow forecasting is very important for realizing urban intelligent traffic system. In the past, many neural network models have been proposed to predict traffic flow, but the effect was not very significant. The reason is that most of them are based on shallow model learning and they are prone to fall into local extreme values and cannot simulate more complex mathematical operations. Therefore, they are not suitable for simulating realistic traffic conditions. As a new subject of machine learning, deep learning has achieved remarkable results in speech and image processing. It can unsupervised abstract effective features from the data for prediction, so in this study, deep learning modeling is used for urban trunk road traffic flow forecasting. The experimental results of this study show that the model is effective in traffic flow forecasting.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shengdong Mou
AU  - Zhengxian Xiong
PY  - 2019/04
DA  - 2019/04
TI  - Urban Traffic Flow Fore-casting based on Deep Learning Model
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 707
EP  - 713
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.113
DO  - 10.2991/icmeit-19.2019.113
ID  - Mou2019/04
ER  -