Causally Aware Spatiotemporal Multigraph Convolutional Network for Accurate and Reliable Traffic Prediction
Published Online:19 Nov 2025https://doi.org/10.1287/ijoc.2024.0891
References
- (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. Preprint, submitted March 4, https://arxiv.org/abs/1803.01271.Google Scholar
- (2018) An order-based algorithm for learning structure of Bayesian networks. Kratochvil V, Studený M, eds. Proc. 9th Internat. Conf. Probabilistic Graphical Models (PMLR, New York), 25–36.Google Scholar
- (2011) Efficient structure learning of Bayesian networks using constraints. J. Machine Learn. Res. 12(20):663–689.Google Scholar
- (2025) Causally aware spatiotemporal multigraph convolutional network for accurate and reliable traffic prediction. https://doi.org/10.1287/ijoc.2024.0891.cd, https://github.com/INFORMSJoC/2024.0891.Google Scholar
- (2024) A spatial-temporal approach for multi-airport traffic flow prediction through causality graphs. IEEE Trans. Intelligent Transportation Systems 25(1):532–544.Crossref, Google Scholar
- (2024) Causal machine learning for predicting treatment outcomes. Nature Medicine 30(4):958–968.Crossref, Google Scholar
- (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. Proc. 33rd AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 3656–3663.Crossref, Google Scholar
- (2021) Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans. Knowledge Data Engrg. 34(11):5415–5428.Crossref, Google Scholar
- (2016) Deep residual learning for image recognition. Proc. 2016 IEEE Conf. Comput. Vision Pattern Recognition (CVPR) (IEEE, New York), 770–778.Google Scholar
- (2008) A tutorial on learning with Bayesian networks. Holmes DE, Jain LC, eds. Innovations in Bayesian Networks (Springer, Berlin), 33–82.Crossref, Google Scholar
- (2014) Big data and its technical challenges. Comm. ACM 57(7):86–94.Crossref, Google Scholar
- (2022) Graph neural network for traffic forecasting: A survey. Expert Systems Appl. 207:117921.Crossref, Google Scholar
- (2023) Uncertainty quantification via spatial-temporal Tweedie model for zero-inflated and long-tail travel demand prediction. Proc. 32nd ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 3983–3987.Google Scholar
- (2024) Spatial-temporal uncertainty-aware graph networks for promoting accuracy and reliability of traffic forecasting. Expert Systems Appl. 238:122143.Crossref, Google Scholar
- (2016) Semi-supervised classification with graph convolutional networks. Preprint, submitted September 9, https://arxiv.org/abs/1609.02907.Google Scholar
- (2014) Design of Modern Communication Networks: Methods and Applications (Academic Press, New York).Google Scholar
- (2018) Distribution-free predictive inference for regression. J. Amer. Statist. Assoc. 113(523):1094–1111.Crossref, Google Scholar
- (2022b) Nonlinear traffic prediction as a matrix completion problem with ensemble learning. Transportation Sci. 56(1):52–78.Link, Google Scholar
- (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. Preprint, submitted July 6, https://arxiv.org/abs/1707.01926.Google Scholar
- (2022a) Deep spatio-temporal adaptive 3D convolutional neural networks for traffic flow prediction. ACM Trans. Intelligent Systems Tech. 13(2):1–21.Google Scholar
- (2024) A fusion pretrained approach for identifying the cause of sarcasm remarks. INFORMS J. Comput. 37(2):465–479.Link, Google Scholar
- (2025) Dynamic causal explanation based diffusion-variational graph neural network for spatiotemporal forecasting. IEEE Trans. Neural Networks Learn. Systems 36(5):9524–9537.Crossref, Google Scholar
- (2022) Conformal prediction with temporal quantile adjustments. Proc. 35th Conf. Neural Information Processing Systems (Curran Associates Inc., Red Hook, NY), 31017–31030.Google Scholar
- (2024) Deterring the gray market: Product diversion detection via learning disentangled representations of multivariate time series. INFORMS J. Comput. 36(2):571–586.Link, Google Scholar
- (2023) Dynamic causal graph convolutional network for traffic prediction. Proc. 2023 IEEE 19th Internat. Conf. Automation Sci. Engrg. (CASE) (IEEE, New York), 1–8.Google Scholar
- (2024) Estimating and mitigating the congestion effect of curbside pick-ups and drop-offs: A causal inference approach. Transportation Sci. 58(2):355–376.Link, Google Scholar
- (2023) A cost-effective sequential route recommender system for taxi drivers. INFORMS J. Comput. 35(5):1098–1119.Link, Google Scholar
- (2020) Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting. Proc. 29th ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 1025–1034.Google Scholar
- (2022) Traffic congestion propagation inference using dynamic Bayesian graph convolution network. Transportation Res. Part C Emerging Tech. 135:103526.Crossref, Google Scholar
- (2024) GT-LSTM: A spatio-temporal ensemble network for traffic flow prediction. Neural Networks 171:251–262.Crossref, Google Scholar
- (2002) Dynamic Bayesian Networks: Representation, Inference and Learning (University of California, Berkeley).Google Scholar
- (1999) Numerical Optimization (Springer, New York).Crossref, Google Scholar
- (2020) DYNOTEARS: Structure learning from time-series data. Chiappa S, Calandra R, eds. Proc. 23rd Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 1595–1605.Google Scholar
- (2018) Spatial as deep: Spatial CNN for traffic scene understanding. Proc. 32nd AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 7276–7283.Google Scholar
- (2023) Uncertainty quantification for traffic forecasting: A unified approach. Proc. 2023 IEEE 39th Internat. Conf. Data Engrg. (ICDE) (IEEE, New York), 992–1004.Google Scholar
- (2024) Towards a unified understanding of uncertainty quantification in traffic flow forecasting. IEEE Trans. Knowledge Data Engrg. 36(5):2239–2256.Crossref, Google Scholar
- (2024) A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models. Transportation Res. Part C Emerging Tech. 162:104585.Crossref, Google Scholar
- (2008) A tutorial on conformal prediction. J. Machine Learn. Res. 9(3):371–421.Google Scholar
- (2022) Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities. Transportation Res. Part C Emerging Tech. 145:103921.Crossref, Google Scholar
- (2021) Conformal time-series forecasting. Proc. 34th Conf. Neural Information Processing Systems (Curran Associates, Inc., New York), 6216–6228.Google Scholar
- (2022) Dual dynamic spatial-temporal graph convolution network for traffic prediction. IEEE Trans. Intelligent Transportation Systems 23(12):23680–23693.Crossref, Google Scholar
- (1997) Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions. J. Amer. Stat. Assoc. 92(437):357–367.Crossref, Google Scholar
- (2017) Attention is all you need. Proc. 30th Conf. Neural Information Processing Systems (Curran Associates, Inc., New York), 6000–6010.Google Scholar
- (2024) Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks. IEEE Trans. Intelligent Transportation Systems 25(8):8770–8781.Crossref, Google Scholar
- (2019) Graph WaveNet for deep spatial-temporal graph modeling. Proc. 28th Internat. Joint Conf. Artificial Intelligence (IJCAI), 1907–1913.Google Scholar
- (2024) Adaptive modeling of uncertainties for traffic forecasting. IEEE Trans. Intelligent Transportation Systems 25(4):4427–4442.Crossref, Google Scholar
- (2022) Modelling traffic as multi-graph signals: Using domain knowledge to enhance the network-level passenger flow prediction in metro systems. J. Rail Transport Planning Management 24:100342.Crossref, Google Scholar
- (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Proc. 27th Internat. Joint Conf. Artificial Intelligence (IJCAI), 3634–3640.Google Scholar
- (2023) Causal conditional hidden Markov model for multimodal traffic prediction. Proc. 37th AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 4929–4936.Google Scholar
- (2018) DAGs with NO TEARS: Continuous optimization for structure learning. Proc. 32nd Conf. Neural Information Processing Systems (Curran Associates Inc., Red Hook, NY), 9492–9503. Google Scholar

