Causally Aware Spatiotemporal Multigraph Convolutional Network for Accurate and Reliable Traffic Prediction

Accurate and reliable prediction has profound implications for a wide range of applications, such as hospital admissions, inventory control, and route planning. In this study, we focus on an instance of spatiotemporal learning problems—traffic prediction—to demonstrate an advanced deep learning model developed for making accurate and reliable predictions. Despite the significant progress in traffic prediction, limited studies have incorporated both explicit (e.g., road network topology) and implicit (e.g., causality-related traffic phenomena and impact of exogenous factors) traffic patterns simultaneously to improve prediction performance. Meanwhile, the variable nature of traffic states necessitates quantifying the uncertainty of model predictions in a statistically principled way; however, extant studies offer no provable guarantee on the statistical validity of confidence intervals in reflecting their actual likelihood of containing the ground truth. In this paper, we propose an end-to-end traffic prediction framework that leverages three primary components to generate accurate and reliable traffic predictions: dynamic causal structure learning for discovering implicit traffic patterns from massive traffic data, causally aware spatiotemporal multigraph convolutional network (CASTMGCN) for learning spatiotemporal dependencies, and conformal prediction for uncertainty quantification. In particular, CASTMGCN fuses several graphs that characterize different important aspects of traffic networks (including physical road structure, time-lagged causal effect, and contemporaneous causal relationships) and an auxiliary graph that captures the effect of exogenous factors on the road network. On this basis, a conformal prediction approach tailored to spatiotemporal data is further developed for quantifying the uncertainty in node-wise traffic predictions over varying prediction horizons. Experimental results on two real-world traffic data sets of varying scale demonstrate that the proposed method outperforms several state-of-the-art models in prediction accuracy; moreover, it generates more efficient prediction regions than several other methods while strictly satisfying the statistical validity in coverage. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This paper was supported by the Hong Kong Research Grants Council [Grant PolyU 25206422], the Research Committee of The Hong Kong Polytechnic University [Project Code G-UARJ, Student Account Code RM5Y], and the National Natural Science Foundation of China [Grants 62406269, 72021002, and 72201180]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0891 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0891 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .