Layerwise Recurrent Autoencoder for Real-World Traffic Flow Forecasting

2019 ◽  
pp. 78-88
Author(s):  
Junhui Zhao ◽  
Tianqi Zhu ◽  
Ruidong Zhao ◽  
Peize Zhao
Keyword(s):  
Traffic Flow ◽  
Real World ◽  
Traffic Flow Forecasting

A Summary of Short-Term Traffic Flow Forecasting Methods

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Gang Chang ◽  
Yi Zhang ◽  
Danya Yao ◽  
Yun Yue
Keyword(s):  
Traffic Flow ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Forecasting Methods

Traffic Flow Forecasting

2001 ◽  
Vol 49 (11/2001) ◽  
Author(s):  
B. S. Kerner ◽  
R. G. Herrtwich
Keyword(s):  
Traffic Flow ◽  
Traffic Flow Forecasting

TrafficBERT: Pre-trained model with large-scale data for long-range traffic flow forecasting

2021 ◽  
pp. 115738
Author(s):  
KyoHoon Jin ◽  
JeongA Wi ◽  
EunJu Lee ◽  
ShinJin Kang ◽  
SooKyun Kim ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Long Range ◽  
Large Scale ◽  
Traffic Flow Forecasting ◽  
Large Scale Data ◽  
Scale Data

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

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