Global-Local Spatiotemporal Perception Model for Traffic Flow Prediction

doi:10.19678/j.issn.1000-3428.0069550

Abstract

Abstract:

Traffic flow prediction is crucial for intelligent transportation systems; however, the existing methods cannot accurately capture the temporal and spatial correlation of traffic data. To further explore the complex spatiotemporal correlation of road networks and improve prediction performance, a spatiotemporal graph attention network GL-STAGGN model considering global-local spatiotemporal perception is proposed. First, the spatiotemporal heterogeneity of traffic flow is represented by embedding the spatiotemporal location of the input data to enhance the feature representation of spatiotemporal data; subsequently, global-local time-aware multi-head self-attention synchronization is used to mine the global and local spatiotemporal dynamic correlation. Second, a graph attention network and a dynamic graph convolutional network based on the attention mechanism are introduced to aggregate local node features and dynamically adjust the spatial correlation intensity for capturing the internal correlation between global and local spatial correlations in depth. Finally, the GL-STAGGN model is constructed using an encoder-decoder architecture to fuse the spatiotemporal components. Experimental results on real-world highway traffic datasets, PEMS04 and PEMS08, show that compared with the advanced method DSTAGNN, which does not consider the global-local spatiotemporal relationship and spatial heterogeneity, the average Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) decreased by 2.8%, 2.3%, and 3.3%, respectively. Furthermore, GL-STAGGN performs better than most existing baseline models in terms of supporting intelligent transportation systems.

Key words: traffic flow prediction, spatio-temporal correlation, encoder-decoder, attention mechanism, dynamic graph convolution network

摘要：

交通流预测方法是智能交通系统的重要基础, 但现有方法在准确捕获交通数据的时空相关性上仍有不足。为挖掘道路网络的复杂时空相关性, 提高预测性能, 提出一种考虑全局-局部时空感知的时空图注意力网络模型GL-STAGGN。首先对输入数据进行时空位置嵌入来表征交通流的时空异质性, 以增强时空数据的特征表示, 其次利用全局-局部时间感知的多头自注意力同步挖掘全局与局部空间范围内的时间动态相关性; 然后引入图注意力网络和基于注意力机制的动态图卷积网络分别聚合局部节点特征和动态调整空间相关性强度, 以深度捕捉全局与局部空间相关性的内在关联; 最后采用编码器-解码器架构将时空组件融合以构成GL-STAGGN模型。在现实世界的高速公路交通数据集PEMS04和PEMS08上的实验结果表明, 相比未考虑全局-局部时空关系和忽略空间异质性的先进方法DSTAGNN, GL-STAGGN的平均绝对误差(MAE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)平均降低了2.8%、2.3%和3.3%, 优于大多数现有基线模型, 可更好地为智能交通系统提供支持。

PAN Lihu, YIN Jiali, ZHANG Rui, XIE Binhong, ZHANG Linliang. Global-Local Spatiotemporal Perception Model for Traffic Flow Prediction[J]. Computer Engineering, 2026, 52(3): 392-402.

潘理虎, 尹佳莉, 张睿, 谢斌红, 张林梁. 面向交通流预测的全局-局部时空感知模型[J]. 计算机工程, 2026, 52(3): 392-402.

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Figures/Tables 9

Fig.1 GL-STAGGN model based on encoder-decoder architecture

Fig.2 Global-local time perception multi-head self-attention

Fig.3 Spatial DGCN

Fig.4 Composition analysis of GL-STAGGN

Fig.5 Component analysis of GL-STAGGN in predicting 12 time steps

Fig.6 Impact of different network configurations on model performance

References 42

1	周玮, 闵卫东. 面向交通场景的强鲁棒性场景图生成网络. 计算机工程, 2025, 51 (9): 231- 241. doi: 10.19678/j.issn.1000-3428.0069154
	ZHOU W , MIN W D . Robust scene graph generation network for traffic scenes. Computer Engineering, 2025, 51 (9): 231- 241. doi: 10.19678/j.issn.1000-3428.0069154
2	JIN G Y , LIANG Y X , FANG Y C , et al. Spatio-temporal graph neural networks for predictive learning in urban computing: a survey. IEEE Transactions on Knowledge and Data Engineering, 2023, 36 (10): 5388- 5408.
3	WILLIAMS B M , HOEL L A . Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. Journal of Transportation Engineering, 2003, 129 (6): 664- 672. doi: 10.1061/(ASCE)0733-947X(2003)129:6(664)
4	ZIVOT E , WANG J . Vector autoregressive models for multivariate time series. Berlin, Germany: Springer, 2006.
5	OKUTANI I , STEPHANEDES Y J . Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological, 1984, 18 (1): 1- 11. doi: 10.1016/0191-2615(84)90002-X
6	WU C H , HO J M , LEE D T . Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 2004, 5 (4): 276- 281. doi: 10.1109/TITS.2004.837813
7	VAN L J W C , VAN H C . Short-term traffic and travel time prediction models. Artificial Intelligence Applications to Critical Transportation Issues, 2012, 22 (1): 22- 41.
8	YU B, YIN H T, ZHU Z X. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/1709.04875.
9	LI Y G, YU R, SHAHABI C, et al. Diffusion convolutional recurrent neural network: data-driven traffic forecasting[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/1707.01926.
10	GUO S N , LIN Y F , FENG N , et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Artificial Intelligence, 2019, 33 (1): 922- 929.
11	ZHENG C P , FAN X L , WANG C , et al. GMAN: a graph multi-attention network for traffic prediction. Artificial Intelligence, 2020, 34 (1): 1234- 1241.
12	SONG C , LIN Y F , GUO S N , et al. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. Artificial Intelligence, 2020, 34 (1): 914- 921.
13	LI M Z , ZHU Z X . Spatial-temporal fusion graph neural networks for traffic flow forecasting. Artificial Intelligence, 2021, 35 (5): 4189- 4196.
14	LAN S Y, MA Y T, HUANG W, et al. DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting[C]//Proceedings of International Conference on Machine Learning. Washington D. C., USA: IEEE Press, 2022: 11906-11917.
15	LUO X L, ZHU C J, ZHANG D T, et al. Dynamic graph convolution network with spatio-temporal attention fusion for traffic flow prediction[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/2310.08328.
16	周楚昊, 林培群. 基于多通道Transformer的交通量预测方法. 计算机应用研究, 2023, 40 (2): 435- 439.
	ZHOU C H , LIN P Q . Traffic flow prediction method based on multi-channel Transformer. Application Research of Computers, 2023, 40 (2): 435- 439.
17	XU X, ZHANG L, LIU B L, et al. Transport-hub-aware spatial-temporal adaptive graph Transformer for traffic flow prediction[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/2310.08328.
18	LIU A Y, ZHANG Y Y. Spatial-temporal interactive dynamic graph convolution network for traffic forecasting[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/2205.08689.
19	FU R, ZHANG Z, LI L. Using LSTM and GRU neural network methods for traffic flow prediction[C]//Proceedings of the 31st Youth Academic Annual Conference of Chinese Association of Automation. Wuhan, China: [s. n. ], 2016: 324-328.
20	CHO K, MERRIENBOER B V, GÜLÇEHRE Ç, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/1406.1078.
21	CUI Z Y , KE R M , PU Z Y , et al. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transportation Research Part C: Emerging Technologies, 2020, 118, 102674. doi: 10.1016/j.trc.2020.102674
22	MA X L , TAO Z M , WANG Y H , et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 2015, 54, 187- 197. doi: 10.1016/j.trc.2015.03.014
23	倪庆剑, 彭文强, 张志政, 等. 基于信息增强传输的时空图神经网络交通流预测. 计算机研究与发展, 2022, 59 (2): 282- 293.
	NI Q J , PENG W Q , ZHANG Z Z , et al. Spatial-temporal graph neural network for traffic flow prediction based on information enhanced transmission. Journal of Computer Research and Development, 2022, 59 (2): 282- 293.
24	CUI Z, KE R, PU Z, et al. Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/1801.02143.
25	VAN DEN OORD A, DIELEMAN S, ZEN H, et al. WaveNet: a generative model for raw audio[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/1609.03499.
26	BAI S, KOLTER J Z, KOLTUN V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/1803.01271.
27	ZHANG J B , ZHENG Y , QI D K . Deep spatio-temporal residual networks for citywide crowd flows prediction. Artificial Intelligence, 2017, 31 (1): 332- 343.
28	YAO H X , TANG X F , WEI H , et al. Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. Artificial Intelligence, 2019, 33 (1): 5668- 5675.
29	HUANG R, HUANG C, LIU Y, et al. LSGCN: long short-term traffic prediction with graph convolutional networks[C]//Proceedings of the IJCAI'20. Washington D. C., USA: IEEE Press, 2020: 2355-2361.
30	ZHAO Y J , LIN Y F , WEN H M , et al. Spatial-temporal position-aware graph convolution networks for traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems, 2022, 24 (8): 8650- 8666.
31	CIRSTEA R G, YANG B, GUO C J, et al. Towards spatio- temporal aware traffic time series forecasting[C]//Proceedings of the 38th IEEE International Conference on Data Engineering. Washington D. C., USA: IEEE Press, 2022: 2900-2913.
32	LIU H Y , ZHU C J , ZHANG D T , et al. Attention-based spatial-temporal graph convolutional recurrent networks for traffic forecasting. Berlin, Germany: Springer, 2023.
33	WU Z, PAN S, LONG G, et al. Graph wavenet for deep spatial-temporal graph modeling[EB/OL]. [2024-02-10]. https://arxiv.org/pdf/1906.00121.
34	ZHANG W Y , ZHU K , ZHANG S , et al. Dynamic graph convolutional networks based on spatiotemporal data embedding for traffic flow forecasting. Knowledge-Based Systems, 2022, 250, 109028. doi: 10.1016/j.knosys.2022.109028
35	KONG J L , FAN X M , ZUO M , et al. ADCT-Net: adaptive traffic forecasting neural network via dual-graphic cross-fused transformer. Information Fusion, 2024, 103, 102122. doi: 10.1016/j.inffus.2023.102122
36	BUI K N , CHO J , YI H . Spatial-temporal graph neural network for traffic forecasting: an overview and open research issues. Applied Intelligence, 2022, 52 (3): 2763- 2774. doi: 10.1007/s10489-021-02587-w
37	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2017: 303-315.
38	BELKIN M, NIYOGI P. Laplacian eigenmaps and spectral techniques for embedding and clustering[C]//Proceedings of Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2002: 585-592.
39	WANG K F , AN J , ZHOU M C , et al. Minority-weighted graph neural network for imbalanced node classification in social networks of Internet of people. IEEE Internet of Things Journal, 2022, 10 (1): 330- 340.
40	YE Y Q , XIAO Y , ZHOU Y X , et al. Dynamic multi-graph neural network for traffic flow prediction incorporating traffic accidents. Expert Systems with Applications, 2023, 234, 121101. doi: 10.1016/j.eswa.2023.121101
41	GUO S N , LIN Y F , WAN H Y , et al. Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Transactions on Knowledge and Data Engineering, 2021, 34 (11): 5415- 5428.
42	STEPHANEDES Y , MICHALOPOULOS P , PLUM R . Comparative performance evaluation of demand and prediction algorithms. Traffic Engineering & Control, 1981, 22 (32): 443- 452.

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