Traffic Flow Prediction via Spatial Temporal Graph Neural Network

By Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, Jian Yu. The Web Conference, 2020

661 citations recorded by the source corpus.

Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation networks. The dynamic of the traffic flow on one road not only depends on the sequential patterns in the temporal dimension but also relies on other roads in the spatial dimension. Although there are existing works on predicting the future traffic flow, the majority of them have certain limitations on modeling spatial and temporal dependencies. In this paper, we propose a novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns. In particular, the framework offers a learnable positional attention mechanism to effectively aggregate information from adjacent roads. Meanwhile, it provides a sequential component to model the traffic flow dynamics which can exploit both local and global temporal dependencies. Experimental results on various real traffic datasets demonstrate the effectiveness of the proposed framework.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/4a4f84992b4ee8331f1e3189f6f9b0437214035c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Haitao Yuan
2. Guoliang Li
3. Junbo Zhang
4. Yu Zheng
5. Yuxuan Liang
6. Zezhi Shao
7. Mengzhang Li
8. Fei Wang

In Control and Systems Engineering

1. Zhanxing Zhu
2. Zezhi Shao
3. Fei Wang
4. Zhao Zhang
5. Yu Zheng
6. Haitao Yuan
7. Wei Wei
8. Guoliang Li

In Transportation

1. Guoliang Li
2. Haitao Yuan
3. Junbo Zhang
4. Yu Zheng
5. Zezhi Shao
6. Mengzhang Li
7. Fei Wang
8. Zhao Zhang

Citations in the corpus

• Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution (2021, methodology): “Additionally, as traffic conditions are highly susceptible to external environment, most traffic prediction works introduce external features such as weather [1], Point of Interest (POI) [2], [22], time of day [1]–[13], [23] and day of week [1], [2] into the model to enhance performance.”
• Spatio-Temporal Meta-Graph Learning for Traffic Forecasting (2022, neutral): “…GCN-based modules (Diao et al. 2019; Guo et al. 2019; Geng et al. 2019; Zhang et al. 2021) and sequence models, such as Recurrent Neural Networks (RNNs) (Li et al. 2018; Bai et al. 2020; Ye et al. 2021), WaveNet (Wu et al. 2019), Transformer (Zheng et al. 2020; Wang et al. 2020; Xu et al. 2020).”
• Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting (2022, methodology): “[36] • SVR: Support Vector Regression (SVR) uses linear support vector machine for classical time series regression task.”
• Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting (2020, methodology): “• STGNN: Spatial Temporal Graph Neural Network, which is also a complicated transformer model with a learnable positional attention mechanism and a sequential component(Wang et al. 2020).”
• DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting (2022, methodology): “Transformer algorithms (Park et al., 2019; Wang et al., 2020) resorted to a self-attention mechanism to model the spatial-temporal correlations.”
• Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, background): “fields [66], [171], and another one is through the integration of different temporal learning networks [16], [19], [115].”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “Also, some people [162, 163] consider the sequential features among historical network flows, so they further append RNN models to encode historical data.”
• PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction (2023, background): “Recently, the attention mechanism has become increasingly popular due to its effectiveness in modeling the dynamic dependencies in traffic data (Guo et al. 2019; Wang et al. 2020; Lin et al. 2020b; Yan and Ma 2021; Ye et al. 2022).”
• Graph Neural Network for Traffic Forecasting: A Survey (2021, neutral): “Problem Relevant Studies Road Traffic Flow [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62],”