#mobility-prediction News & Analysis

2 articles tagged with #mobility-prediction. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Jun 95/10

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DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling

DynaOD is a machine learning framework that generates realistic urban mobility patterns by modeling temporal dynamics through discrete directional trends and continuous evolution, without requiring historical origin-destination data. The approach uses semantic temporal signals to condition pretrained OD generators, achieving better accuracy and distributional fidelity than existing methods with cross-city transferability.

AINeutralarXiv – CS AI · Jun 46/10

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Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent

Researchers introduce AgentMob, a training-free LLM-driven agent framework that improves mobility prediction by using adaptive evidence gathering rather than static prompts. The system achieves strong performance on multiple datasets by distinguishing routine cases from ambiguous ones, with significant accuracy improvements on difficult prediction scenarios.

🧠 GPT-5