ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting
Abstract
:1. Introduction
- We propose attentive diffusion convolution to uncover unseen spatial dependencies from traffic graph signals automatically and further present the multi-diffusion convolution block to harvest spatial features in various manners. Extensive experiments demonstrate the ability of our MDC to improve the results when the graph structure is false or unknown.
- We construct a novel deep learning hybrid network, the ST-TrafficNet, for spatial-temporal traffic forecasting. The holistic ST-TrafficNet is effective and efficient to capture spatial-temporal features with cascading spatial-temporal layers by adopting residual connections. The core idea of the spatial-temporal layer is to enable our proposed MDC block to tackle spatial dependencies of traffic graph signals with high-dimension temporal features extracted by stacked LSTM block.
- We evaluate ST-TrafficNet on two benchmark datasets and compare it with various baseline methods for traffic forecasting. The experiments show that our proposed method achieves state-of-the-art results in terms of three widely used criteria.
2. Related Works
3. Preliminary
3.1. Traffic Forecasting Modeling
3.2. Graph Diffusion Convolution
3.3. Graph Attention Mechanism
4. Methodology
4.1. Spatial Aware Multi-Diffusion Convolution Block
4.2. Temporal Aware Stacked LSTM Block
4.3. Framework of Spatial-temporal Deep Leaning Network
5. Experiments and Discussion
5.1. Data Preparation
5.2. Baseline Methods
5.3. Experimental Setup and Evaluation Criteria
5.4. Performance Comparison
5.5. Efficacy of Multi-Diffusion Convolution Block
5.6. Influence of Missing and Incorrect Graph Structure Knowledge
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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METR-LA Dataset | 15 min | 30 min | 60 min | ||||||
---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | MAPE | MAE | RMSE | MAPE | MAE | RMSE | MAPE | |
(Vehs) | (Vehs) | (%) | (Vehs) | (Vehs) | (%) | (Vehs) | (Vehs) | (%) | |
HA | 4.16 | 7.80 | 13.00 | 4.16 | 7.80 | 13.00 | 4.16 | 7.80 | 13.00 |
ARIMA | 3.99 | 8.21 | 9.60 | 5.15 | 10.45 | 12.70 | 6.90 | 13.23 | 17.40 |
LSVR | 2.97 | 5.89 | 7.68 | 3.64 | 7.35 | 9.90 | 4.67 | 9.13 | 13.63 |
FNN | 3.99 | 7.94 | 9.91 | 4.23 | 8.17 | 12.92 | 4.49 | 8.69 | 14.01 |
FC-LSTM | 3.44 | 6.30 | 9.60 | 3.77 | 7.23 | 10.90 | 4.37 | 8.69 | 13.20 |
WaveNet | 2.99 | 5.89 | 8.04 | 3.59 | 7.28 | 10.25 | 4.45 | 8.93 | 13.62 |
STGCN | 2.88 | 5.74 | 7.62 | 3.47 | 7.24 | 9.57 | 4.59 | 9.40 | 12.70 |
DCRNN | 2.77 | 5.38 | 7.30 | 3.15 | 6.45 | 8.80 | 3.60 | 7.60 | 10.50 |
ST-UNet | 2.83 | 5.17 | 7.03 | 3.22 | 6.36 | 8.63 | 3.65 | 7.40 | 10.00 |
GWaveNet | 2.70 | 5.15 | 6.92 | 3.09 | 6.22 | 8.37 | 3.55 | 7.37 | 10.01 |
ST-TrafficNet | 2.56 | 5.06 | 6.82 | 2.89 | 6.17 | 8.35 | 3.46 | 7.29 | 9.89 |
PEMS-BAY Dataset | 15 min | 30 min | 60 min | ||||||
---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | MAPE | MAE | RMSE | MAPE | MAE | RMSE | MAPE | |
(Vehs) | (Vehs) | (%) | (Vehs) | (Vehs) | (%) | (Vehs) | (Vehs) | (%) | |
HA | 2.88 | 5.59 | 6.80 | 2.88 | 5.59 | 6.80 | 2.88 | 5.59 | 6.80 |
ARIMA | 1.62 | 3.30 | 3.50 | 2.33 | 4.76 | 5.40 | 3.38 | 6.50 | 8.30 |
LSVR | 1.42 | 3.45 | 3.31 | 2.13 | 4.37 | 5.28 | 2.34 | 4.28 | 5.55 |
FNN | 1.59 | 3.42 | 3.53 | 2.11 | 4.42 | 5.16 | 3.18 | 6.24 | 8.12 |
FC-LSTM | 2.05 | 4.19 | 4.80 | 2.20 | 4.55 | 5.20 | 2.37 | 4.96 | 5.70 |
WaveNet | 1.39 | 3.01 | 2.91 | 1.83 | 4.21 | 4.16 | 2.35 | 5.43 | 5.87 |
STGCN | 1.36 | 2.96 | 2.90 | 1.81 | 4.27 | 4.17 | 2.49 | 5.69 | 5.79 |
DCRNN | 1.38 | 2.95 | 2.90 | 1.74 | 3.97 | 3.90 | 2.37 | 4.94 | 5.30 |
ST-UNet | 1.38 | 2.83 | 2.79 | 1.72 | 3.82 | 3.75 | 1.97 | 4.63 | 4.83 |
GWaveNet | 1.31 | 2.74 | 2.73 | 1.63 | 3.70 | 3.67 | 1.98 | 4.65 | 4.92% |
ST-TrafficNet | 1.26 | 2.72 | 2.68 | 1.58 | 3.57 | 3.59 | 1.93 | 4.61 | 4.88 |
Data-Set | Model | 15 min | 30 min | 60 min | ||||||
---|---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | MAPE | MAE | RMSE | MAPE | MAE | RMSE | MAPE | ||
(Vehs) | (Vehs) | (%) | (Vehs) | (Vehs) | (%) | (Vehs) | (Vehs) | (%) | ||
T(Temporal only) | 3.36 | 6.27 | 9.61 | 3.75 | 7.21 | 10.92 | 4.34 | 8.65 | 13.21 | |
METR- | (Attentive only) | 2.81 | 5.32 | 7.36 | 3.12 | 6.46 | 8.56 | 3.69 | 7.48 | 10.42 |
LA | (without Attentive) | 2.68 | 5.19 | 7.01 | 2.95 | 6.31 | 8.48 | 3.57 | 7.36 | 10.01 |
(proposed model) | 2.56 | 5.06 | 6.82 | 2.89 | 6.17 | 8.35 | 3.46 | 7.29 | 9.89 | |
T(Temporal only) | 2.02 | 4.14 | 4.78 | 2.20 | 4.54 | 5.19 | 2.34 | 4.94 | 5.70 | |
PEMS- | (Attentive only) | 1.41 | 2.86 | 2.79 | 1.74 | 3.80 | 3.91 | 2.13 | 4.78 | 5.17 |
BAY | (without Attentive) | 1.33 | 2.74 | 2.75 | 1.66 | 3.68 | 3.73 | 2.01 | 4.69 | 5.00 |
(proposed model) | 1.26 | 2.72 | 2.68 | 1.58 | 3.57 | 3.59 | 1.93 | 4.61 | 4.88 |
Model | Computational Time | |
---|---|---|
Train (Seconds/Epoch) | Predict (Seconds) | |
ARIMA | - | 0.94 |
ST-TrafficNet | 83.74 | 3.52 |
Disturbance | ST-TrafficNet | ST-TrafficNet | |
---|---|---|---|
() | () | ||
original | 1.33 | 1.26 | |
noise | 5% | 1.37 | 1.26 |
10% | 1.44 | 1.29 | |
original | 1.33 | 1.26 | |
missing | 50% | 1.57 | 1.29 |
100% | 2.02 | 1.41 |
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Lu, H.; Huang, D.; Song, Y.; Jiang, D.; Zhou, T.; Qin, J. ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting. Electronics 2020, 9, 1474. https://doi.org/10.3390/electronics9091474
Lu H, Huang D, Song Y, Jiang D, Zhou T, Qin J. ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting. Electronics. 2020; 9(9):1474. https://doi.org/10.3390/electronics9091474
Chicago/Turabian StyleLu, Huakang, Dongmin Huang, Youyi Song, Dazhi Jiang, Teng Zhou, and Jing Qin. 2020. "ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting" Electronics 9, no. 9: 1474. https://doi.org/10.3390/electronics9091474
APA StyleLu, H., Huang, D., Song, Y., Jiang, D., Zhou, T., & Qin, J. (2020). ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting. Electronics, 9(9), 1474. https://doi.org/10.3390/electronics9091474