AIDec 1, 2024

BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis

arXiv:2412.00953v122 citationsh-index: 3Has CodeICDE
Originality Incremental advance
AI Analysis

This addresses the need for integrated analysis in real-world applications like navigation apps, though it is incremental as it builds on existing spatiotemporal foundation models.

The paper tackles the problem of separately analyzing trajectory and traffic state data in spatiotemporal applications by introducing BIGCity, a unified model that jointly handles both data types and multiple tasks, achieving state-of-the-art performance across 8 tasks and outperforming 18 baselines.

Typical dynamic ST data includes trajectory data (representing individual-level mobility) and traffic state data (representing population-level mobility). Traditional studies often treat trajectory and traffic state data as distinct, independent modalities, each tailored to specific tasks within a single modality. However, real-world applications, such as navigation apps, require joint analysis of trajectory and traffic state data. Treating these data types as two separate domains can lead to suboptimal model performance. Although recent advances in ST data pre-training and ST foundation models aim to develop universal models for ST data analysis, most existing models are "multi-task, solo-data modality" (MTSM), meaning they can handle multiple tasks within either trajectory data or traffic state data, but not both simultaneously. To address this gap, this paper introduces BIGCity, the first multi-task, multi-data modality (MTMD) model for ST data analysis. The model targets two key challenges in designing an MTMD ST model: (1) unifying the representations of different ST data modalities, and (2) unifying heterogeneous ST analysis tasks. To overcome the first challenge, BIGCity introduces a novel ST-unit that represents both trajectories and traffic states in a unified format. Additionally, for the second challenge, BIGCity adopts a tunable large model with ST task-oriented prompt, enabling it to perform a range of heterogeneous tasks without the need for fine-tuning. Extensive experiments on real-world datasets demonstrate that BIGCity achieves state-of-the-art performance across 8 tasks, outperforming 18 baselines. To the best of our knowledge, BIGCity is the first model capable of handling both trajectories and traffic states for diverse heterogeneous tasks. Our code are available at https://github.com/bigscity/BIGCity

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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