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The field of urban spatial-temporal prediction is advancing rapidly with thedevelopment of deep learning techniques and the availability of large-scaledatasets. However, challenges persist in accessing and utilizing diverse urbanspatial-temporal datasets from different sources and stored in differentformats, as well as determining effective model structures and components withthe proliferation of deep learning models. This work addresses these challengesand provides three significant contributions. Firstly, we introduce "atomicfiles", a unified storage format designed for urban spatial-temporal big data,and validate its effectiveness on 40 diverse datasets, simplifying datamanagement. Secondly, we present a comprehensive overview of technologicaladvances in urban spatial-temporal prediction models, guiding the developmentof robust models. Thirdly, we conduct extensive experiments using diversemodels and datasets, establishing a performance leaderboard and identifyingpromising research directions. Overall, this work effectively manages urbanspatial-temporal data, guides future efforts, and facilitates the developmentof accurate and efficient urban spatial-temporal prediction models. It canpotentially make long-term contributions to urban spatial-temporal datamanagement and prediction, ultimately leading to improved urban livingstandards.

Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]