Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction

• URL: http://arxiv.org/abs/2306.09882v2
• Date: Wed, 31 Jan 2024 01:46:58 GMT
• Title: Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
• Authors: Xinke Jiang, Dingyi Zhuang, Xianghui Zhang, Hao Chen, Jiayuan Luo, Xiaowei Gao
• Abstract summary: We propose the Spatial-Temporal Tweedie Graph Neural Network (STTD) to address the sparse and long-tail characteristics in high-resolution O-D matrices. Our evaluations show STTD's superiority in providing accurate predictions and precise confidence intervals, particularly in high-resolution scenarios.
• Score: 5.9489254626049926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatial-temporal 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 real-world datasets highlight STTD's superiority in providing accurate predictions and precise confidence intervals, particularly in high-resolution scenarios.

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