DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling
arXiv:2606.09086v1 Announce Type: new Abstract: Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations. A key challenge is to translate semantic temporal signals into temporally coherent OD patterns while preserving the inherent spatial heterogeneity of urban regions. We propose DynaOD, a semantic-driven framework that models temporal dynamics through two complementary perspectives: discrete directional trends that characterize qualitative shifts in urban activity patterns, and c
The proliferation of urban data and advancements in AI interpretative models are enabling more sophisticated analyses of complex temporal-spatial dynamics.
This research provides a method for generating realistic mobility patterns without historical data, which is crucial for urban planning, disaster response, and logistics in areas with limited data.
The ability to simulate dynamic origin-destination flows purely from temporal context reduces reliance on exhaustive historical datasets, expanding the applicability of smart city solutions.
- · Urban planners
- · Logistics companies
- · Smart city technology developers
- · AI model developers
- · Entities reliant on extensive historical mobility data procurement
- · Legacy urban simulation techniques
Improved predictive models for urban traffic, resource allocation, and public services based on real-time and projected needs.
Reduced infrastructure costs and improved efficiency in new urban developments or post-disaster reconstruction due to better initial planning.
Enhanced AI-driven urban autonomy, where systems can predict and adapt to mobility needs with minimal human oversight.
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Read at arXiv cs.AI