.png){kind=icon}
返回
Spatial-Temporal Conv-Sequence Learning With Accident Encoding for Traffic Flow Prediction
.png){kind=icon}
OA
AI
DOI：10.1109/TNSE.2022.3152983.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In an intelligent transportation system, the key problem of traffic forecasting is how to extract periodic temporal dependencies and complex spatial correlations. Current state-of-the-art methods for predicting traffic flow are based on graph architectures and sequence learning models, but they do not fully exploit dynamic spatial-temporal information in the traffic system. Specifically, the temporal dependencies in the short-range are diluted by recurrent neural networks. Moreover, local spatial information is also ignored by existing sequence models, because their convolution operation uses global average pooling. Besides, accidents may occur during object transition, which will cause congestion in the real world and further decrease prediction accuracy. To overcome these challenges, we propose Spatial-Temporal Conv-sequence Learning (STCL), where a focused temporal block uses unidirectional convolution to capture short-term periodic temporal dependencies effectively, and a spatial-temporal fusion module is responsible for extracting dependencies of interactions and decreasing the feature dimensions. Moreover, as the accidents features have an impact on local traffic congestion, we employ position encoding to detect anomalies in complex traffic situations. We have conducted a large number of experiments on real-world tasks and verified the effectiveness of our proposed method.
Keyword:
Encoding
Accidents
Market research
Traffic congestion
Shape
Roads
Fluctuations
Sequence learning
spatial-temporal
accident
traffic prediction
期刊
I
IF:
7.9
论文数:
2.6K
被引数:
10.0K
机构
引用论文
Isolated Giant Smooth Muscle Fibres in Beroe Ovata: Ionic Dependence of Action Potentials Reveals two Distinct Types of Fibre 卵子中的孤立的巨大平滑肌纤维: 动作电位的离子依赖性揭示了两种不同类型的纤维
PREAI
分享收藏分享收藏Comparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression properties.png)
RSC Advances
RSC AdvancesIF0
PREAI
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Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic ForecastingIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning用于交通流预测的深度架构: 具有多任务学习的深度信念网络
PREAI
分享收藏
查看更多内容