基于Graph Transformer的大规模路网交通流量预测

doi:10.3969/j.issn.1007-7375.2023.05.018

工业工程 ›› 2023, Vol. 26 ›› Issue (5): 159-167.doi: 10.3969/j.issn.1007-7375.2023.05.018

• 系统建模与优化算法 • 上一篇

基于Graph Transformer的大规模路网交通流量预测

董成祥¹, 魏昕¹, 张坤鹏², 汪永超³

1. 广东工业大学机电工程学院，广东广州，510006;
2. 河南工业大学电气工程学院，河南郑州，450001;
3. 广州番禺职业技术学院智能制造学院，广东广州，511483

收稿日期:2022-12-26 发布日期:2023-10-25
作者简介:董成祥(1989-),男,河南省人,博士研究生,主要研究方向为智能交通系统和自动驾驶汽车。
基金资助:
国家自然科学基金资助项目 (62002101)；广东省普通高校特色创新项目 (2022KTSCX293)；广州市教育局高校科研项目 (202235232)

Traffic Flow Prediction for Large-scale Road Network Based on Graph Transformer

DONG Chengxiang¹, WEI Xin¹, ZHANG Kunpeng², WANG Yongchao³

1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China;
2. College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China;
3. School of Intelligent Manufacturing, Guangzhou Panyu Polytechnic, Guangzhou 511483, China

Received:2022-12-26 Published:2023-10-25

摘要/Abstract

摘要： 为了捕捉大规模路网交通流量复杂、动态的时空特征，实现大规模路网交通流量的准确预测，建立了基于Graph Transformer 的交通流预测模型。该模型运用GRU模块提取路网中历史交通流量的时间特征，根据分布在路网中的传感器之间的关联，建立以历史交通流量为节点、传感器间的相互联系为边的交通图。在此基础上，运用基于Graph Transformer的深度学习技术进行时空特征的提取。为了验证该预测模型的性能，基于PeMS高速公路数据集与6种基线模型进行对比，实验表明本文提出的预测模型展现了最优的预测性能。

关键词: 大规模路网, 门控循环单元, 时空特征, 交通流量

Abstract: To accurately predict traffic flow in large-scale road networks, a traffic flow prediction model is proposed based on Graph Transformer to capture the complex and dynamic spatiotemporal characteristics of traffic flows. Gate recurrent unit (GRU) module is adopted in the model to extract temporal features of historical traffic flow data in a road network. According to the connections among sensors distributed in a network, traffic graphs are then established with historical traffic flows as nodes and connections of sensors as edges. On this basis, spatiotemporal characteristics are captured using Graph Transformer-based deep learning technologies. To verify the effectiveness of the proposed model, comparisons with six baseline models using the PeMS highway dataset are conducted. Experiments show that the proposed prediction model results in the best performance.

Key words: large-scale road network, gate recurrent unit (GRU), spatiotemporal characteristics, traffic flow

中图分类号:

U491

董成祥, 魏昕, 张坤鹏, 汪永超. 基于Graph Transformer的大规模路网交通流量预测[J]. 工业工程, 2023, 26(5): 159-167.

DONG Chengxiang, WEI Xin, ZHANG Kunpeng, WANG Yongchao. Traffic Flow Prediction for Large-scale Road Network Based on Graph Transformer[J]. Industrial Engineering Journal, 2023, 26(5): 159-167.

参考文献

[1] MEDINA-SALGADO B, SANCHEZ-DELACRUZ E, POZOS-PARRA P, et al. Urban traffic flow prediction techniques: A review[J]. Sustainable Computing:Informatics and Systems, 2022, 35: 100739
[2] GUO J, HUANG W, WILLIAMS B M. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification[J]. Transportation Research Part C:Emerging Technologies, 2014, 43: 50-64
[3] WILLIAMS B M, HOEL L A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results[J]. Journal of Transportation Engineering, 2003, 129(6): 664-72
[4] KUMAR S V, VANAJAKSHI L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J]. European Transport Research Review, 2015, 7(3): 1-9
[5] XIE Y, ZHANG Y, YE Z. Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition[J]. Computer Aided Civil and Infrastructure Engineering, 2007, 22(5): 326-34
[6] MODI S, BHATTACHARYA J, BASAK P. Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies[J]. Expert Systems with Applications, 2022, 189: 116140
[7] YU B, SONG X, GUAN F, et al. k-Nearest neighbor model for multiple-time-step prediction of short-term traffic condition[J]. Journal of Transportation Engineering, 2016, 142(6): 04016018
[8] YANG Y, LU H. Short-term traffic flow combined forecasting model based on SVM[C]//Proceedings of the 2010 International Conference on Computational and Information Sciences. US: IEEE, 2010: 262-265.
[9] SHARMA B, KUMAR S, TIWARI P, et al. ANN based short-term traffic flow forecasting in undivided two lane highway[J]. Journal of Big Data, 2018, 5(1): 1-16
[10] YANG B, SUN S, LI J, et al. Traffic flow prediction using LSTM with feature enhancement[J]. Neurocomputing, 2019, 332: 320-7
[11] DAI G, MA C, XU X. Short-term traffic flow prediction method for urban road sections based on space– time analysis and GRU[J]. IEEE Access, 2019, 7: 143025-35
[12] CHEN C, LI K, TEO S G, et al. Exploiting spatio-temporal correlations with multiple 3d convolutional neural networks for citywide vehicle flow prediction[C]//Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM) . Singapore: IEEE, 2018: 893-898.
[13] ZHENG Z, YANG Y, LIU J, et al. Deep and embedded learning approach for traffic flow prediction in urban informatics[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3927-3939
[14] CHENG X, ZHANG R, ZHOU J, et al. DeepTransport: Learning spatial-temporal dependency for traffic condition forecasting[C]//Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil: IEEE, 2018: 1-8.
[15] DU S, LI T, GONG X, et al. A hybrid method for traffic flow forecasting using multimodal deep learning[J]. International Journal of Computational Intelligence Systems, 2020, 13(1): 85-97
[16] WANG H, ZHANG R, CHENG X, et al. Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 16137-16147
[17] 董成祥, 魏昕, 张坤鹏, 等. 基于图卷积网络的乘客打车需求预测[J]. 工业工程, 2022, 25(5): 98-105
DONG Chengxiang, WEI Xin, ZHANG Kunpeng, et al. Passenger ride-hailing demand prediction based on graph convolutional networksl[J]. Industrial Engineering Journal, 2022, 25(5): 98-105
[18] 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, 2019, 21(9): 3848-3858
[19] WU Z, PAN S, LONG G, et al. Graph wavenet for deep spatial-temporal graph modeling [C]//IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence. Menlo Park, California: AAAI Press, 2019: 1907-1913
[20] LI Z, XIONG G, CHEN Y, et al. A hybrid deep learning approach with GCN and LSTM for traffic flow prediction[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC) . Auckland: IEEE, 2019: 1929-1933
[21] LI S, JIN X, XUAN Y, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting [C/OL]//Proceeding of NeurIPS 2019. Vancouver, Canada: (2019-06-29) . https://doi.org/10.48550/arXiv.1907.00235.
[22] REZA S, FERREIRA M C, MACHADO J, et al. A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks[J]. Expert Systems with Applications, 2022, 202: 117275
[23] DONG C, ZHANG K, WEI X, et al. Spatiotemporal graph attention network modeling for multi-step passenger demand prediction at multi-zone level[J]. Physica A:Statistical Mechanics and Its Applications, 2022, 603: 127789

基于Graph Transformer的大规模路网交通流量预测

Traffic Flow Prediction for Large-scale Road Network Based on Graph Transformer

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