Urban Traffic Flow Fore-casting based on Deep Learning Model
Shengdong Mou, Zhengxian Xiong
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.113How to use a DOI?
- Deep learning; Traffic flow forecasting; Neural network; Machine learning.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 707 EP - 713 SN - 2352-538X UR - https://doi.org/10.2991/icmeit-19.2019.113 DO - https://doi.org/10.2991/icmeit-19.2019.113 ID - Mou2019/04 ER -