.png){kind=icon}
返回
A Variational Bayesian Inference-Based En-Decoder Framework for Traffic Flow Prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2023.3276216.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for traffic flow prediction for making travel plans. The speed of traffic flow can be affected by roads condition, weather, holidays, etc. Moreover, sensors to catch the information about traffic flow will be interfered with by environmental factors such as illumination, collection time, occlusion, etc. Therefore, the traffic flow in the practical transportation system is complicated, uncertain, and challenging to predict accurately. Motivated from the aforementioned issues and challenges, in this paper, we propose a deep encoder-decoder prediction framework based on variational Bayesian inference. A Bayesian neural network is designed by combining variational inference with Gated Recurrent Units (GRU) which is used as the deep neural network unit of the encoder-decoder framework to mine the intrinsic dynamics of traffic flow. Then, the variational inference is introduced into the multi-head attention mechanism to avoid noise-induced deterioration of prediction accuracy. The proposed model achieves superior prediction performance on the Guangzhou urban traffic flow dataset over the benchmarks, particularly when the long-term prediction.
Keyword:
Traffic flow prediction
time-series data prediction
variational Bayesian inference
multi-head attention
deep learning
encoder-decoder
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
Multivariate time series forecasting via attention-based encoder-decoder framework.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction基于注意力的conv-lstm混合深度学习模型的短时交通流预测
OAAI
OAAI分享收藏Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting流量图卷积循环神经网络: 用于网络规模流量学习和预测的深度学习框架OAAI
OAAI分享收藏Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network使用变分贝叶斯卷积门递归单元网络对太阳辐射进行集成时空预测APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏分享收藏
查看更多内容