Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (10): 3114-3120.DOI: 10.11772/j.issn.1001-9081.2022101587
• Artificial intelligence • Previous Articles
Received:
2022-10-24
Revised:
2023-03-13
Accepted:
2023-03-14
Online:
2023-10-07
Published:
2023-10-10
Contact:
Mengling WANG
About author:
GAO Chun, born in 1998, M. S. candidate. Her research interests include traffic big data mining.
Supported by:
通讯作者:
王梦灵
作者简介:
高醇(1998—),女,云南曲靖人,硕士研究生,主要研究方向:交通大数据挖掘;
基金资助:
CLC Number:
Chun GAO, Mengling WANG. Highway traffic flow prediction based on feature fusion graph attention network[J]. Journal of Computer Applications, 2023, 43(10): 3114-3120.
高醇, 王梦灵. 基于特征融合图注意网络的高速公路交通流预测[J]. 《计算机应用》唯一官方网站, 2023, 43(10): 3114-3120.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022101587
类别 | 模型 | PeMSD4 | PeMSD8 | ||
---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | ||
经典 时间 序列 | HA | 54.14 | 36.76 | 44.03 | 29.52 |
ARIMA | 68.13 | 32.11 | 43.30 | 24.04 | |
VAR | 51.73 | 33.76 | 31.21 | 21.41 | |
LSTM | 45.82 | 29.45 | 36.96 | 23.18 | |
深度 学习 | STGCN | 38.41 | 27.28 | 30.78 | 20.99 |
GeoMAN | 37.84 | 23.64 | 28.91 | 17.84 | |
ASTGCN | 36.29 | 23.52 | 28.16 | 18.61 | |
GCN-GAN | 36.24 | 23.39 | 28.07 | 18.52 | |
FF-GAT | 35.56 | 22.89 | 27.19 | 16.79 |
Tab. 1 Comparison of results of different models on PeMSD4 and PeMSD8 datasets
类别 | 模型 | PeMSD4 | PeMSD8 | ||
---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | ||
经典 时间 序列 | HA | 54.14 | 36.76 | 44.03 | 29.52 |
ARIMA | 68.13 | 32.11 | 43.30 | 24.04 | |
VAR | 51.73 | 33.76 | 31.21 | 21.41 | |
LSTM | 45.82 | 29.45 | 36.96 | 23.18 | |
深度 学习 | STGCN | 38.41 | 27.28 | 30.78 | 20.99 |
GeoMAN | 37.84 | 23.64 | 28.91 | 17.84 | |
ASTGCN | 36.29 | 23.52 | 28.16 | 18.61 | |
GCN-GAN | 36.24 | 23.39 | 28.07 | 18.52 | |
FF-GAT | 35.56 | 22.89 | 27.19 | 16.79 |
模型 | PeMSD4 | PeMSD8 | ||
---|---|---|---|---|
RMSE | MAE | RMSE | MAE | |
MA | 36.44 | 23.55 | 28.19 | 17.67 |
MF | 36.27 | 23.33 | 27.74 | 17.21 |
FF-GAT | 35.56 | 22.89 | 27.19 | 16.79 |
Tab. 2 Performance of different modules on PeMSD4 and PeMSD8 datasets
模型 | PeMSD4 | PeMSD8 | ||
---|---|---|---|---|
RMSE | MAE | RMSE | MAE | |
MA | 36.44 | 23.55 | 28.19 | 17.67 |
MF | 36.27 | 23.33 | 27.74 | 17.21 |
FF-GAT | 35.56 | 22.89 | 27.19 | 16.79 |
1 | CHEN L, SHAO W, LV M, et al. AARGNN: an attentive attributed recurrent graph neural network for traffic flow prediction considering multiple dynamic factors[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 17201-17211. 10.1109/tits.2022.3171451 |
2 | SHIN Y, YOON Y. Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(3): 2082-2092. 10.1109/tits.2020.3031331 |
3 | HUANG J, LUO K, CAO L, et al. Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 20681-20695. 10.1109/tits.2022.3173689 |
4 | 王海起,王志海,李留珂,等. 基于网格划分的城市短时交通流量时空预测模型[J]. 计算机应用, 2022, 42(7):2274-2280. |
WANG H Q, WANG Z H, LI L K, et al. Spatial-temporal prediction model of urban short-term traffic flow based on grid division[J]. Journal of Computer Applications, 2022, 42(7):2274-2280. | |
5 | LIN Z, FENG J, LU Z, et al. DeepSTN+: context-aware spatial-temporal neural network for crowd flow prediction in metropolis[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2019: 1020-1027. 10.1609/aaai.v33i01.33011020 |
6 | 陈丹蕾,陈红,任安虎. 考虑时空影响下的图卷积网络短时交通流预测[J]. 计算机工程与应用, 2021, 57(13):269-275. 10.3778/j.issn.1002-8331.2006-0175 |
CHEN D L, CHEN H, REN A H. Short-time traffic flow prediction of graph convolutional network considering influence of space and time[J]. Computer Engineering and Applications, 2021, 57(13):269-275. 10.3778/j.issn.1002-8331.2006-0175 | |
7 | SONG C, LIN Y, GUO S, et al. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2020: 914-921. 10.1609/aaai.v34i01.5438 |
8 | ZHAO L, SONG Y, ZHANG C, et al. T-GCN: a temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(9): 3848-3858. 10.1109/tits.2019.2935152 |
9 | ZHU J, HAN X, DENG H, et al. KST-GCN: a knowledge-driven spatial-temporal graph convolutional network for traffic forecasting[J]. IEEE Transactions on Intelligent Transaction Systems, 2022, 23(9): 15055-15065. 10.1109/tits.2021.3136287 |
10 | GUO S, LIN Y, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2019: 922-929. 10.1609/aaai.v33i01.3301922 |
11 | LIANG Y, KE S, ZHANG J, et al. GeoMAN: multi-level attention networks for geo-sensory time series prediction[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2018: 3428-3434. 10.24963/ijcai.2018/476 |
12 | YIN X, WU G, WEI J, et al. Multi-stage attention spatial-temporal graph networks for traffic prediction[J]. Neurocomputing, 2021, 428: 42-53. 10.1016/j.neucom.2020.11.038 |
13 | DEFFERRARD M, BRESSON X, VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2016: 3844-3852. |
14 | BRUNA J, ZAREMBA W, SZLAM A, et al. Spectral networks and locally connected networks on graphs[EB/OL]. (2014-05-21) [2022-05-23].. 10.1017/cbo9780511761942.003 |
15 | VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph attention networks[EB/OL]. (2018-02-04) [2022-05-23].. |
16 | GREENSHIELDS B D, BIBBINS J R, CHANNING W S, et al. A study of traffic capacity[J]. Highway Research Board Proceedings, 1935, 14: 448-477. |
17 | CHEN C, PETTY K, SKABARDONIS A, et al. Freeway performance measurement system: mining loop detector data[J]. Transportation Research Record, 2001, 1748(1): 96-102. 10.3141/1748-12 |
18 | HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. 10.1162/neco.1997.9.8.1735 |
19 | YU B, YIN H, ZHU Z. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2018:3634-3640. 10.24963/ijcai.2018/505 |
20 | ZHENG H, LI X, LI Y, et al. GCN-GAN: integrating graph convolutional network and generative adversarial network for traffic flow prediction[J]. IEEE Access, 2022, 10: 94051-94062. 10.1109/access.2022.3204036 |
21 | SETHI J K, MITTAL M. Analysis of air quality using univariate and multivariate time series models[C]// Proceedings of the 10th International Conference on Cloud Computing, Data Science and Engineering. Piscataway: IEEE, 2020: 823-827. 10.1109/confluence47617.2020.9058303 |
22 | RUN L, MIN L X, LU Z X. Research and comparison of ARIMA and grey prediction models for subway traffic forecasting[C]// Proceedings of the 2020 International Conference on Intelligent Computing, Automation and Systems. Piscataway: IEEE, 2020:63-67. 10.1109/icicas51530.2020.00020 |
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