Traffic flow prediction occupies a pivotal position in intelligent transportation systems, and accurate traffic flow prediction is of great significance for alleviating traffic congestion and reducing the incidence of traffic accidents. To improve the accuracy of traffic flow forecasts, it is necessary to consider the historical data over a longer period. However, most of the existing methods only consider part of the recent historical time information, ignoring the implied fluctuation of the traffic flow in some regions in the historical contemporaneous time interval. Therefore, we propose a multidimensional long-term spatio-temporal attention model for traffic flow forecasting by capturing time series correlations. In this model, we design a multi-temporal dimensional attention mechanism and a deep fusion extraction convolutional neural network to capture multidimensional temporal information and fuse spatio-temporal correlations to predict traffic flow. The experimental results on two real datasets show that the proposed model outperforms the compared models.
JinWLinYWuZ, et al.Spatio-temporal recurrent convolutional networks for citywide short-term crowd flows prediction. In: Proceedings of the 2nd International conference on compute and data analysis, 2018, pp.28–35. https://doi.org/10.1145/3193077.319308.
2.
HuangXMaCZhaoY, et al.A hybrid model of neural network with VMD–CNN–GRU for traffic flow prediction. Int J Mod Phys C2023; 34: 2350159.
3.
LiuYWuCWenJ, et al.A grey convolutional neural network model for traffic flow prediction under traffic accidents. Neurocomputing2022; 500: 761–775.
4.
YangDLiSPengZ, et al.MF-CNN: traffic flow prediction using convolutional neural network and multi-features fusion. IEICE Trans Inf Syst2019; 102: 1526–1536.
5.
ZhangWYuYQiY, et al.Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transport A: Trans Sci2019; 15: 1688–1711.
6.
RenCChaiCYinC, et al.Short-term traffic flow prediction: a method of combined deep learnings. J Adv Transport2021; 2021: 9928073.
7.
SoniRRoyPNagwanshiKK. WKNN-FDCNN method for big data driven traffic flow prediction in its. Multimed Tools Appl2024; 83: 25261–25286.
8.
QiaoYWangYMaC, et al.Short-term traffic flow prediction based on 1DCNN-LSTM neural network structure. Mod Phys Lett B2021; 35: 2150042.
9.
FuTYuQLaoH, et al.Traffic safety oriented multi-intersection flow prediction based on transformer and CNN. Sec Commun Netw2023; 2023: 1363639.
10.
HuangXYeYDingW, et al.Multi-mode dynamic residual graph convolution network for traffic flow prediction. Inf Sci (Ny)2022; 609: 548–564.
11.
HuangXYeYYangX, et al.Multi-view dynamic graph convolution neural network for traffic flow prediction. Expert Syst Appl2023; 222: 119779.
12.
ZhangZJiaoX. A spatio-temporal grammar graph attention network with adaptive edge information for traffic flow prediction. Appl Intell2023; 53: 28787–28803.
13.
BaoYLiuJShenQ, et al.PKET-GCN: prior knowledge enhanced time-varying graph convolution network for traffic flow prediction. Inf Sci (Ny)2023; 634: 359–381.
14.
YangHLiZQiY. Predicting traffic propagation flow in urban road network with multi-graph convolutional network. Complex Intell Syst2024; 10: 23–35.
15.
NiQZhangM. STGMN: a gated multi-graph convolutional network framework for traffic flow prediction. Appl Intell2022; 52: 15026–15039.
16.
LiQXuPHeD, et al.Multi-source information fusion graph convolution network for traffic flow prediction. Expert Syst Appl2024; 252: 124288.
17.
LuoMDouHZhengN. Spatiotemporal prediction of urban traffics based on deep GNN. Comput Mater Continua2024; 78: 265–282. https://doi.org/10.32604/cmc.2023.040067.
18.
LiSLiJLanJ, et al.Traffic flow prediction based on attention mechanism convolutional neural network. In: International conference on AI and mobile services, 2023, pp.50–59. Springer. https://doi.org/10.1007/978-3-031-45140-9_5.
19.
WangXShangYLiG. DTM-GCN: a traffic flow prediction model based on dynamic graph convolutional network. Multimed Tools Appl2024; 83: 89545–89561. https://doi.org/10.1007/s11042-024-18348-z.
20.
YanJZhangLGaoY, et al.GECRAN: Graph embedding based convolutional recurrent attention network for traffic flow prediction. Expert Syst Appl2024; 125001.
21.
ChenJZhengLHuY, et al.Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction. Inform Fusion2024; 104: 102146.
22.
ChauhanNSKumarNEskandarianA. A novel confined attention mechanism driven BI-GRU model for traffic flow prediction. IEEE trans Intell Transp Syst2024.
23.
AliAUllahIAhmadS, et al.An attention-driven spatio-temporal deep hybrid neural networks for traffic flow prediction in transportation systems. IEEE trans Intell Transp Syst2025.
24.
XuYLiuM. GPT4TFP: Spatio-temporal fusion large language model for traffic flow prediction. Neurocomputing2025; 129562.
25.
KongXZhangJWeiX, et al.Adaptive spatial-temporal graph attention networks for traffic flow forecasting. Appl Intell2022; 1–17. DOI: https://doi.org/10.1007/s10489-021-02648-0.
26.
LiuJKangYLiH, et al.STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting. Appl Intell2023; 53: 12472–12488.
ZhongJJMaYNiuXZ, et al.Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction. J Elect Sci Technol2024; 22: 100244.
29.
BaiDXiaDHuangD, et al.Spatial-temporal graph neural network based on gated convolution and topological attention for traffic flow prediction. Appl Intell2023; 53: 30843–30864.
30.
WuJLLuMWangCY. Forecasting metro rail transit passenger flow with multiple-attention deep neural networks and surrounding vehicle detection devices. Appl Intell2023; 53: 18531–18546.
31.
ZhaoWZhangSWangB, et al.Spatio-temporal causal graph attention network for traffic flow prediction in intelligent transportation systems. PeerJ Comput Sci2023; 9: e1484.
32.
LinJLinCYeQ. Attention based convolutional networks for traffic flow prediction. Multimed Tools Appl2024; 83: 7379–7394.
33.
MoghaddasiKRajabiSSoleimanian GharehchopoghF, et al.An energy-efficient data offloading strategy for 5G-enabled vehicular edge computing networks using double deep Q-network. Wirel Pers Commun2023; 133: 2019–2064.
34.
GharehchopoghFS. Multi-objective secure task offloading strategy for blockchain-enabled IoV-MEC systems: a double deep Q-network approach, IEEE Access 2024; 12: 3437–3463.
35.
MoghaddasiKRajabiSGharehchopoghFS, et al.An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the internet of things. Sustain Comput: Inform Syst2024; 43: 100992.
36.
MinHRahmaniAMGhaderkourehpazP, et al.A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks. Ad Hoc Netw2025; 166: 103656.
37.
MakridakisSHibonM. ARMA models and the Box–Jenkins methodology. J Forecast1997; 16: 147–163.
38.
WuCHHoJMLeeDT. Travel-time prediction with support vector regression. IEEE trans Intell Transp Syst2004; 5: 276–281.
39.
SutskeverIVinyalsOLeQV. Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst2014; 27.
40.
ZhengCFanXWangC, et al.GMAN: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI conference on artificial intelligence, Vol. 34, pp.1234–1241. https://doi.org/10.1609/aaai.v34i01.5477.
41.
YinXWuGWeiJ, et al.Multi-stage attention spatial-temporal graph networks for traffic prediction. Neurocomputing2021; 428: 42–53.
