Jiang, Xinke; Zhuang, Dingyi; Zhang, Xianghui; Chen, Hao; Luo, Jiayuan; Gao, Xiaowei; (2023) Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (pp. pp. 3983-3987). ACM: Birmingham, UK.

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Abstract

Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatialtemporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within these matrices, which challenge the Gaussian assumption inherent to deterministic deep learning models. To address these challenges, we propose a novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional ’zero-inflated’ model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using realworld datasets highlight STTD’s superiority in providing accurate predictions and precise confidence intervals, particularly in highresolution scenarios. GitHub code is available online.

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