Bayesian graph convolutional network for traffic prediction
Authors: 
Jun Fu, Wei Zhou, Zhibo ChenAuthors Info & Claims
Jun Fu, Wei Zhou, Zhibo ChenAuthors Info & ClaimsReferences
[1]
Guerrini F., Traffic congestion costs Americans $124 billion a year, report says, 2014, Forbes, October, 14.
[2]
Ahmed M.S., Cook A.R., Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques, 1979.
[3]
Holden K., Vector auto regression modeling and forecasting, J. Forecast. 14 (3) (1995) 159–166.
[4]
Kipf T.N., Welling M., Semi-supervised classification with graph convolutional networks, 2016, arXiv preprint arXiv:1609.02907.
[5]
Wu Z., Pan S., Long G., Jiang J., Zhang C., Graph wavenet for deep spatial-temporal graph modeling, 2019, arXiv preprint arXiv:1906.00121.
[6]
Dong C.J., Shao C.F., Zhuge C.-X., Meng M., Spatial and temporal characteristics for congested traffic on urban expressway, J. Beijing Polytech. Univ. 38 (8) (2012) 128–132.
[7]
Y. Rong, W. Huang, T. Xu, J. Huang, Dropedge: Towards deep graph convolutional networks on node classification, in: International Conference on Learning Representations, 2019.
[8]
Liu J., Guan W., A summary of traffic flow forecasting methods, J. Highw. Transp. Res. Develop. 3 (2004) 82–85.
[9]
Guo J., Huang W., Williams B.M., Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification, Transp. Res. C 43 (2014) 50–64.
[10]
Lee S., Fambro D.B., Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting, Transp. Res. Rec. 1678 (1) (1999) 179–188.
[11]
Williams B.M., Hoel L.A., Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results, J. Transp. Eng. 129 (6) (2003) 664–672.
[12]
Hou Y., Edara P., Chang Y., Road network state estimation using random forest ensemble learning, in: 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC, IEEE, 2017, pp. 1–6.
[13]
Wu C.-H., Ho J.-M., Lee D.-T., Travel-time prediction with support vector regression, IEEE Trans. Intell. Transp. Syst. 5 (4) (2004) 276–281.
[14]
Sun S., Zhang C., Yu G., A Bayesian network approach to traffic flow forecasting, IEEE Trans. Intell. Transp. Syst. 7 (1) (2006) 124–132.
[15]
Zhang X.-l., He G.-g., Lu H.-p., Short-term traffic flow forecasting based on K-nearest neighbors non-parametric regression, J. Syst. Eng. 24 (2) (2009) 178–183.
[16]
Habtemichael F.G., Cetin M., Short-term traffic flow rate forecasting based on identifying similar traffic patterns, Transp. Res. C 66 (2016) 61–78.
[17]
Sun B., Cheng W., Goswami P., Bai G., Short-term traffic forecasting using self-adjusting k-nearest neighbours, IET Intell. Transp. Syst. 12 (1) (2018) 41–48.
[18]
Park D., Rilett L.R., Forecasting freeway link travel times with a multilayer feedforward neural network, Comput.-Aided Civ. Infrastruct. Eng. 14 (5) (1999) 357–367.
[19]
Lv Y., Duan Y., Kang W., Li Z., Wang F.-Y., Traffic flow prediction with big data: a deep learning approach, IEEE Trans. Intell. Transp. Syst. 16 (2) (2014) 865–873.
[20]
Jia Y., Wu J., Du Y., Traffic speed prediction using deep learning method, in: 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC, IEEE, 2016, pp. 1217–1222.
[21]
Hochreiter S., Schmidhuber J., Long short-term memory, Neural Comput. 9 (8) (1997) 1735–1780.
[22]
Cho K., Van Merriënboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y., Learning phrase representations using RNN encoder-decoder for statistical machine translation, 2014, arXiv preprint arXiv:1406.1078.
[23]
Ma X., Tao Z., Wang Y., Yu H., Wang Y., Long short-term memory neural network for traffic speed prediction using remote microwave sensor data, Transp. Res. C 54 (2015) 187–197.
[24]
Yu R., Li Y., Shahabi C., Demiryurek U., Liu Y., Deep learning: A generic approach for extreme condition traffic forecasting, in: Proceedings of the 2017 SIAM International Conference on Data Mining, SIAM, 2017, pp. 777–785.
[25]
Zhao Z., Chen W., Wu X., Chen P.C., Liu J., LSTM network: A deep learning approach for short-term traffic forecast, IET Intell. Transp. Syst. 11 (2) (2017) 68–75.
[26]
Laptev N., Yosinski J., Li L.E., Smyl S., Time-series extreme event forecasting with neural networks at uber, International Conference on Machine Learning, vol. 34, 2017, pp. 1–5.
[27]
Tian Y., Zhang K., Li J., Lin X., Yang B., LSTM-based traffic flow prediction with missing data, Neurocomputing 318 (2018) 297–305.
[28]
Wu Y., Tan H., Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework, 2016, arXiv preprint arXiv:1612.01022.
[29]
Yu H., Wu Z., Wang S., Wang Y., Ma X., Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks, Sensors 17 (7) (2017) 1501.
[30]
Wang J., Gu Q., Wu J., Liu G., Xiong Z., Traffic speed prediction and congestion source exploration: A deep learning method, in: 2016 IEEE 16th International Conference on Data Mining, ICDM, IEEE, 2016, pp. 499–508.
[31]
Zhang J., Zheng Y., Qi D., Li R., Yi X., Li T., Predicting citywide crowd flows using deep spatio-temporal residual networks, Artificial Intelligence 259 (2018) 147–166.
[32]
Zhang Y., Wang S., Chen B., Cao J., Huang Z., Trafficgan: Network-scale deep traffic prediction with generative adversarial nets, IEEE Trans. Intell. Transp. Syst. 22 (1) (2019) 219–230.
[33]
Du B., Peng H., Wang S., Bhuiyan M.Z.A., Wang L., Gong Q., Liu L., Li J., Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction, IEEE Trans. Intell. Transp. Syst. 21 (3) (2019) 972–985.
[34]
Zhao L., Song Y., Zhang C., Liu Y., Wang P., Lin T., Deng M., Li H., T-gcn: A temporal graph convolutional network for traffic prediction, IEEE Trans. Intell. Transp. Syst. (2019).
[35]
Li Y., Yu R., Shahabi C., Liu Y., Diffusion convolutional recurrent neural network: Data-driven traffic forecasting, 2017, arXiv preprint arXiv:1707.01926.
[36]
Yu B., Yin H., Zhu Z., Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting, 2017, arXiv preprint arXiv:1709.04875.
[37]
Lv M., Hong Z., Chen L., Chen T., Zhu T., Ji S., Temporal multi-graph convolutional network for traffic flow prediction, IEEE Trans. Intell. Transp. Syst. (2020).
[38]
Peng H., Wang H., Du B., Bhuiyan M.Z.A., Ma H., Liu J., Wang L., Yang Z., Du L., Wang S., et al., Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting, Inform. Sci. 521 (2020) 277–290.
[39]
R. Huang, C. Huang, Y. Liu, G. Dai, W. Kong, LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks, in: IJCAI, 2020, pp. 2355–2361.
[40]
Guo S., Lin Y., Feng N., Song C., Wan H., Attention based spatial-temporal graph convolutional networks for traffic flow forecasting, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 2019, pp. 922–929.
[41]
X. Feng, J. Guo, B. Qin, T. Liu, Y. Liu, Effective Deep Memory Networks for Distant Supervised Relation Extraction, in: IJCAI, 2017, pp. 4002–4008.
[42]
Song C., Lin Y., Guo S., Wan H., Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 914–921.
[43]
Bai L., Yao L., Li C., Wang X., Wang C., Adaptive graph convolutional recurrent network for traffic forecasting, 2020, arXiv preprint arXiv:2007.02842.
[44]
Guo G., Yuan W., Short-term traffic speed forecasting based on graph attention temporal convolutional networks, Neurocomputing 410 (2020) 387–393.
[45]
Li W., Wang X., Zhang Y., Wu Q., Traffic flow prediction over muti-sensor data correlation with graph convolution network, Neurocomputing 427 (2021) 50–63.
[46]
Yin X., Wu G., Wei J., Shen Y., Qi H., Yin B., Multi-stage attention spatial-temporal graph networks for traffic prediction, Neurocomputing 428 (2021) 42–53.
[47]
Chen W., Chen L., Xie Y., Cao W., Gao Y., Feng X., Multi-range attentive bicomponent graph convolutional network for traffic forecasting, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 3529–3536.
[48]
Zheng C., Fan X., Wang C., Qi J., Gman: A graph multi-attention network for traffic prediction, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 1234–1241.
[49]
B. Jiang, Z. Zhang, D. Lin, J. Tang, B. Luo, Semi-supervised learning with graph learning-convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 11313–11320.
[50]
S. Yan, Y. Xiong, D. Lin, Spatial temporal graph convolutional networks for skeleton-based action recognition, in: Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
[51]
Xu J., Zhou W., Chen Z., Blind omnidirectional image quality assessment with viewport oriented graph convolutional networks, 2020, arXiv preprint arXiv:2002.09140.
[52]
Sun S., Yu T., Xu J., Lin J., Zhou W., Chen Z., GraphIQA: Learning distortion graph representations for blind image quality assessment, 2021, arXiv preprint arXiv:2103.07666.
[53]
You Y., Chen T., Sui Y., Chen T., Wang Z., Shen Y., Graph contrastive learning with augmentations, Adv. Neural Inf. Process. Syst. 33 (2020) 5812–5823.
[54]
Zhang Y., Pal S., Coates M., Ustebay D., Bayesian graph convolutional neural networks for semi-supervised classification, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 2019, pp. 5829–5836.
[55]
Pal S., Regol F., Coates M., Bayesian graph convolutional neural networks using non-parametric graph learning, 2019, arXiv preprint arXiv:1910.12132.
[56]
Kipf T.N., Welling M., Variational graph auto-encoders, 2016, arXiv preprint arXiv:1611.07308.
[57]
Kalofolias V., Perraudin N., Large scale graph learning from smooth signals, 2017, arXiv preprint arXiv:1710.05654.
[58]
Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (1) (2014) 1929–1958.
[59]
Ermagun A., Chatterjee S., Levinson D., Using temporal detrending to observe the spatial correlation of traffic, PLoS One 12 (5) (2017).
[60]
Paszke A., Gross S., Chintala S., Chanan G., Yang E., DeVito Z., Lin Z., Desmaison A., Antiga L., Lerer A., Automatic differentiation in Pytorch, 2017.
[61]
Kingma D.P., Ba J., Adam: A method for stochastic optimization, 2014, arXiv preprint arXiv:1412.6980.
[62]
Jiang J., Han C., Zhao W.X., Wang J., PDFormer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction, 2023, arXiv preprint arXiv:2301.07945.
[63]
J. Wang, J. Jiang, W. Jiang, C. Li, W.X. Zhao, Libcity: An open library for traffic prediction, in: Proceedings of the 29th International Conference on Advances in Geographic Information Systems, 2021, pp. 145–148.
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Published: 09 July 2024
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