#urban-computing News & Analysis

7 articles tagged with #urban-computing. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles

AIBullisharXiv – CS AI · Jun 237/10

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An Efficient and Effective Architecture for Large-Scale Traffic Prediction via Geometry-Adaptive Square Partitioning

Researchers introduce SqLinear, a neural network architecture that improves traffic prediction scalability by replacing attention mechanisms with efficient linear interactions and using a geometry-adaptive partitioning algorithm. The approach achieves 2.3-5.8% accuracy improvements while reducing training time by up to 30.8% on large-scale traffic datasets.

AIBullisharXiv – CS AI · Mar 37/104

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UrbanFM: Scaling Urban Spatio-Temporal Foundation Models

Researchers developed UrbanFM, a foundation model for urban spatio-temporal data that can analyze traffic patterns and city dynamics across over 100 global cities. The model demonstrates zero-shot generalization capabilities, meaning it can make predictions for unseen cities without additional training, potentially revolutionizing urban planning and smart city applications.

AINeutralarXiv – CS AI · Jun 106/10

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MoE Enhanced Federated Learning for Spatiotemporal Prediction

Researchers propose MoE-FedTP, a federated learning framework using Mixture-of-Experts networks to improve traffic prediction across cities while preserving privacy. The system enables data-rich cities to share knowledge with data-scarce regions by dynamically fusing expert networks tailored to different urban environments, achieving superior accuracy without centralized data collection.

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 56/10

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CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting

Researchers introduce CausalPOI, a spatio-temporal graph-based machine learning framework designed to predict check-in patterns for newly opened Points of Interest by modeling causal relationships between locations. The approach outperforms existing methods by capturing functional dependencies between POIs rather than relying solely on proximity, offering improved forecasting accuracy for urban planning applications.

AINeutralarXiv – CS AI · Jun 16/10

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Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting

Researchers propose GC-MoE, a graph-conditioned mixture of experts framework that improves traffic forecasting by assigning specialized neural network experts to different road segments based on graph topology. The approach trains only 17K parameters while leveraging 1.5M frozen expert weights, achieving competitive results across four standard traffic prediction benchmarks.

AINeutralarXiv – CS AI · May 125/10

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Efficient Prompt Learning for Traffic Forecasting

Researchers propose SimpleST, a lightweight prompt tuning framework that enhances spatio-temporal graph neural networks' ability to generalize across different traffic prediction scenarios. By keeping pre-trained model parameters fixed while adapting through efficient prompting, the approach reduces computational overhead while improving accuracy on real-world urban datasets.