From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
arXiv:2606.09392v1 Announce Type: new Abstract: Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal intervals to reduce storage and computation costs. However, such coarse-grained data severely limits downstream applications that require predictions at a finer temporal granularity. Collecting and maintaining fine-grained traffic data across all locations and time periods would impose a substantial burden on database storage and prepr
This paper addresses a contemporary challenge in AI-driven urban management, as the increasing demand for granular predictions clashes with data storage and processing limitations.
Improving the efficiency of spatio-temporal data management for traffic prediction will enable more accurate, real-time decision-making for logistics, smart cities, and autonomous systems.
The ability to generate fine-grained traffic predictions from coarse-grained data reduces the need for expensive infrastructure to collect and store extensive fine-grained datasets, making advanced prediction more accessible.
- · Logistics companies
- · Smart city developers
- · Autonomous vehicle developers
- · Urban planners
- · Companies relying on expensive, fine-grained data collection
- · Legacy traffic management systems
More efficient urban mobility and resource allocation due to improved traffic prediction accuracy.
Reduced operational costs for businesses and public services that depend on transport networks.
Potential for new business models built on optimized real-time spatio-temporal data services beyond just traffic.
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Read at arXiv cs.AI