OSM+: Cloud-native Open Street Map Data System for City-wide Experiments
Keywords: Global dataset, Traffic prediction, Traffic policy control
Abstract: Road network data can provide rich information about cities and thus become the base for various urban research. However, processing large-volume world-wide road network data requires intensive computing resources and the processed results might be different to be unified for benchmark downstream tasks. Therefore, in this paper, we process the OpenStreetMap data and release a structured world-wide 1-billion-node road network graph database with high accessibility and usability. We have presented three illustrative use cases, traffic prediction task, city boundary detection task and traffic policy control task. Moreover, for the well-investigated traffic prediction task, we release a new benchmark with 31 datasets, which is much more comprehensive than the previously frequently-used datasets. While for the relatively novel traffic policy control task, we release a new 6 city datasets with much larger scale than the previous datasets. Along with the OSM+ dataset, the release of data converters facilitates the integration of multimodal spatial-temporal data based on map information for large model training, thereby expediting the process of uncovering compelling scientific insights.
Primary Area: datasets and benchmarks
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Submission Number: 6732
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