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Traffic forecasting is important for the success of intelligent transportation systems.
Graph hierarchical convolutional recurrent neural network (ghcrnn) for vehicle condition prediction
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Spatial-temporal transformer networks for traffic flow forecasting
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Dynamic spatiotemporal graph neural network with tensor network
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Spatial-temporal dynamic graph attention networks for ride-hailing demand prediction
Pian, W., & Wu, Y. (2020) · 2006
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Jin, G., Xi, Z., Sha, H., Feng, Y., & Huang, J. (2020b) · 2007
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The graph neural network model
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Xie, Y., Xiong, Y., & Zhu, Y. (2020c) · 2008
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A context integrated relational spatio-temporal model for demand and supply forecasting
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Spatial-temporal demand forecasting and competitive supply via graph convolutional networks
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Bayesian spatio-temporal graph convolutional network for traffic forecasting
Fu, J., Zhou, W., & Chen, Z. (2020) · 2010
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Wang, C., Zhang, K., Wang, H., & Chen, B. (2020a) · 2010
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T-drive: driving directions based on taxi trajectories
Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., & Huang, Y. (2010) · 2010
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Deep learning for human mobility: a survey on data and models
Luca, M., Barlacchi, G., Lepri, B., & Pappalardo, L. (2020) · 2012
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Uncertainty intervals for graph-based spatio-temporal traffic prediction
Maas, T., & Bloem, P. (2020) · 2012
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Spectral networks and deep locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2014) · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · 2014
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Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., & LeCun, Y. (2015) · 2015
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Diffusion-convolutional neural networks
Atwood, J., & Towsley, D. (2016) · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · 2016
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Variational graph auto-encoders
Kipf, T. N., & Welling, M. (2016) · 2016
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Arjovsky, M., Chintala, S., & Bottou, L. (2017) · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., & Leskovec, J. (2017) · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017) · 2017
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Deep spatio-temporal residual networks for citywide crowd flows prediction
Zhang, J., Zheng, Y., & Qi, D. (2017) · 2017
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Bike flow prediction with multi-graph convolutional networks
Chai, D., Wang, L., & Yang, Q. (2018) · 2018
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Recurrent multi-graph neural networks for travel cost prediction
Hu, J., Guo, C., Yang, B., Jensen, C. S., & Chen, L. (2018) · 2018
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Geospatial data to images: A deep-learning framework for traffic forecasting
Jiang, W., & Zhang, L. (2018) · 2018
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Graph cnns for urban traffic passenger flows prediction
Li, J., Peng, H., Liu, L., Xiong, G., Du, B., Ma, H., Wang, L., & Bhuiyan, M. Z. A. (2018a) · 2018
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Deep sequence learning with auxiliary information for traffic prediction
Liao, B., Zhang, J., Wu, C., McIlwraith, D., Chen, T., Yang, S., Guo, Y., & Wu, F. (2018) · 2018
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Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
Lin, L., He, Z., & Peeta, S. (2018) · 2018
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Graph cnn+ lstm framework for dynamic macroscopic traffic congestion prediction
Mohanty, S., & Pozdnukhov, A. (2018) · 2018
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Few-shot learning with graph neural networks
Satorras, V. G., & Estrach, J. B. (2018) · 2018
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Machine learning for spatiotemporal sequence forecasting: A survey
Shi, X., & Yeung, D.-Y. (2018) · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018) · 2018
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Graph attention lstm network: A new model for traffic flow forecasting
Wu, T., Chen, F., & Wan, Y. (2018a) · 2018
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Yu, B., Yin, H., & Zhu, Z. (2018) · 2018
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Gaan: Gated attention networks for learning on large and spatiotemporal graphs
Zhang, J., Shi, X., Xie, J., Ma, H., King, I., & Yeung, D. Y. (2018a) · 2018
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Kernel-weighted graph convolutional network: A deep learning approach for traffic forecasting
Zhang, Q., Jin, Q., Chang, J., Xiang, S., & Pan, C. (2018b) · 2018
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Explainability techniques for graph convolutional networks
Baldassarre, F., & Azizpour, H. (2019) · 2019
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What lies beneath: A note on the explainability of black-box machine learning models for road traffic forecasting
Barredo-Arrieta, A., Laña, I., & Del Ser, J. (2019) · 2019
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Gated residual recurrent graph neural networks for traffic prediction
Chen, C., Li, K., Teo, S. G., Zou, X., Wang, K., Wang, J., & Zeng, Z. (2019) · 2019
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Graph attention recurrent neural networks for correlated time series forecasting
Cirstea, R.-G., Guo, C., & Yang, B. (2019) · 2019
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Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting
Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2019) · 2019
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Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting
Diao, Z., Wang, X., Zhang, D., Liu, Y., Xie, K., & He, S. (2019) · 2019
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Gstnet: Global spatial-temporal network for traffic flow prediction
Fang, S., Zhang, Q., Meng, G., Xiang, S., & Pan, C. (2019) · 2019
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Temporal graph convolutional networks for traffic speed prediction considering external factors
Ge, L., Li, H., Liu, J., & Zhou, A. (2019a) · 2019
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Traffic speed prediction with missing data based on tgcn
Ge, L., Li, H., Liu, J., & Zhou, A. (2019b) · 2019
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A multi-step traffic speed forecasting model based on graph convolutional lstm
Guo, J., Song, C., & Wang, H. (2019a) · 2019
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Bikenet: Accurate bike demand prediction using graph neural networks for station rebalancing
Guo, R., Jiang, Z., Huang, J., Tao, J., Wang, C., Li, J., & Chen, L. (2019b) · 2019
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Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks
Han, Y., Wang, S., Ren, Y., Wang, C., Gao, P., & Chen, G. (2019) · 2019
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Piecewise stationary modeling of random processes over graphs with an application to traffic prediction
Hasanzadeh, A., Liu, X., Duffield, N., & Narayanan, K. R. (2019) · 2019
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Online traffic speed estimation for urban road networks with few data: A transfer learning approach
James, J. (2019) · 2019
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Graph convolutional networks for road networks
Jepsen, T. S., Jensen, C. S., & Nielsen, T. D. (2019) · 2019
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Learning dynamic graph embedding for traffic flow forecasting: A graph self-attentive method
Kang, Z., Xu, H., Hu, J., & Pei, X. (2019) · 2019
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Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
Kim, T. S., Lee, W. K., & Sohn, S. Y. (2019) · 2019
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Learning to propagate for graph meta-learning
Liu, L., Zhou, T., Long, G., Jiang, J., & Zhang, C. (2019) · 2019
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Leveraging graph neural network with lstm for traffic speed prediction
Forecaster: A graph transformer for forecasting spatial and time-dependent data
Li, Y., & Moura, J. M. (2020) · 2020
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A two-stream graph convolutional neural network for dynamic traffic flow forecasting
Li, Z., Li, L., Peng, Y., & Tao, X. (2020d) · 2020
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D3p: Data-driven demand prediction for fast expanding electric vehicle sharing systems
Luo, M., Du, B., Klemmer, K., Zhu, H., Ferhatosmanoglu, H., & Wen, H. (2020) · 2020
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Temporal multi-graph convolutional network for traffic flow prediction
Lv, M., Hong, Z., Chen, L., Chen, T., Zhu, T., & Ji, S. (2020) · 2020
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Graph-partitioning-based diffusion convolution recurrent neural network for large-scale traffic forecasting
Mallick, T., Balaprakash, P., Rask, E., & Macfarlane, J. (2020) · 2020
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Lu, Z., Lv, W., Xie, Z., Du, B., & Huang, R. (2019b) · 2019
Cited alongside, same era.
Spatio-temporal deep graph infomax
Opolka, F. L., Solomon, A., Cangea, C., Veličković, P., Liò, P., & Hjelm, R. D. (2019) · 2019
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Urban traffic prediction from spatio-temporal data using deep meta learning
Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., & Zhang, J. (2019) · 2019
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Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review
Pavlyuk, D. (2019) · 2019
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Explainability methods for graph convolutional neural networks
Pope, P. E., Kolouri, S., Rostami, M., Martin, C. E., & Hoffmann, H. (2019) · 2019
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Transfer knowledge between sub-regions for traffic prediction using deep learning method
Ren, Y., & Xie, K. (2019) · 2019
Cited alongside, same era.
Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling
Wang, Y., Yin, H., Chen, H., Wo, T., Xu, J., & Zheng, K. (2019) · 2019
Cited alongside, same era.
Transfer learning with graph neural networks for short-term highway traffic forecasting
Mallick, T., Balaprakash, P., Rask, E., & Macfarlane, J. (2021) · 2020
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A comprehensive evaluation of deep learning-based techniques for traffic prediction
Mena-Oreja, J., & Gozalvez, J. (2020) · 2020
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Region-wide congestion prediction and control using deep learning
Mohanty, S., Pozdnukhov, A., & Cassidy, M. (2020) · 2020
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Stp-trellisnets: Spatial-temporal parallel trellisnets for metro station passenger flow prediction
Ou, J., Sun, J., Zhu, Y., Jin, H., Liu, Y., Zhang, F., Huang, J., & Wang, X. (2020) · 2020
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Spatio-temporal meta learning for urban traffic prediction
Pan, Z., Zhang, W., Liang, Y., Zhang, W., Yu, Y., Zhang, J., & Zheng, Y. (2020) · 2020
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St-grat: A novel spatio-temporal graph attention networks for accurately forecasting dynamically changing road speed
Park, C., Lee, C., Bahng, H., Tae, Y., Jin, S., Kim, K., Ko, S., & Choo, J. (2020) · 2020
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Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting
Peng, H., Wang, H., Du, B., Bhuiyan, M. Z. A., Ma, H., Liu, J., Wang, L., Yang, Z., Du, L., Wang, S. et al. (2020) · 2020
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Resgcn: Residual graph convolutional network based free dock prediction in bike sharing system
Qin, T., Liu, T., Wu, H., Tong, W., & Zhao, S. (2020b) · 2020
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Topological graph convolutional network-based urban traffic flow and density prediction
Qiu, H., Zheng, Q., Msahli, M., Memmi, G., Qiu, M., & Lu, J. (2020) · 2020
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Modeling local and global flow aggregation for traffic flow forecasting
Qu, Y., Zhu, Y., Zang, T., Xu, Y., & Yu, J. (2020) · 2020
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Traffic forecasting using temporal line graph convolutional network: Case study
Ramadan, A., Elbery, A., Zorba, N., & Hassanein, H. S. (2020) · 2020
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Gannster: Graph-augmented neural network spatio-temporal reasoner for traffic forecasting
Sánchez, C. S., Wieder, A., Sottovia, P., Bortoli, S., Baumbach, J., & Axenie, C. (2020) · 2020
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Estimation of urban travel time with sparse traffic surveillance data
Shao, K., Wang, K., Chen, L., & Zhou, Z. (2020) · 2020
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Ttpnet: A neural network for travel time prediction based on tensor decomposition and graph embedding
Shen, Y., Jin, C., & Hua, J. (2020) · 2020
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Predicting origin-destination flow via multi-perspective graph convolutional network
Shi, H., Yao, Q., Guo, Q., Li, Y., Zhang, L., Ye, J., Li, Y., & Liu, Y. (2020) · 2020
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Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting
Shin, Y., & Yoon, Y. (2020) · 2020
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Graph attention convolutional network: Spatiotemporal modeling for urban traffic prediction
Song, Q., Ming, R., Hu, J., Niu, H., & Gao, M. (2020b) · 2020
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Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks
Sun, J., Zhang, J., Li, Q., Yi, X., Liang, Y., & Zheng, Y. (2020) · 2020
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Traffic flow prediction model based on spatio-temporal dilated graph convolution
Sun, X., Li, J., Lv, Z., & Dong, C. (2020) · 2020
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Sun, Y., Wang, Y., Fu, K., Wang, Z., Zhang, C., & Ye, J. (2021) · 2020
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Dynamic spatial-temporal graph attention graph convolutional network for short-term traffic flow forecasting
Tang, C., Sun, J., & Sun, Y. (2020a) · 2020
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Tedjopurnomo, D. A., Bao, Z., Zheng, B., Choudhury, F., & Qin, A. (2020) · 2020
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St-mgat: Spatial-temporal multi-head graph attention networks for traffic forecasting
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Spatial-temporal graph attention networks for traffic flow forecasting
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Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks
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Urban traffic flow forecast based on fastgcrnn
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