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

arXiv:2308.12899v3 Announce Type: replace
Abstract: The field of urban spatial-temporal prediction is advancing rapidly with the development of deep learning techniques and the availability of large-scale datasets. However, challenges persist in accessing and utilizing diverse urban spatial-temporal datasets from different sources and stored in different formats, as well as determining effective model structures and components with the proliferation of deep learning models. This work addresses these challenges and provides three significant contributions. Firstly, we introduce "atomic files", a unified storage …

abstract analysis arxiv availability benchmark challenges cs.lg data data management datasets deep learning deep learning techniques development diverse evaluation experiment however management performance prediction scale spatial temporal type unified data urban