MobiWM is a multimodal world model for mobile networks that learns state-action dynamics to enable unlimited-horizon counterfactual traffic simulations and optimization.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
6 Pith papers cite this work. Polarity classification is still indexing.
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IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
GenTTP is a generalized predictor that learns to forecast network-wide travel times and flows for arbitrary route choice distributions rather than only typical daily patterns.
SA-HGNN with contrastive learning improves power outage prediction by modeling spatial effects of extreme weather on infrastructure across multiple utility territories.
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.
FASE pairs a spatiotemporal graph neural network and multivariate Hawkes process for crime prediction with a fairness-constrained linear program for patrol allocation, showing that allocation fairness holds in simulation but a 3.5 percentage point detection gap between minority and non-minority ZIPs
citing papers explorer
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Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation
MobiWM is a multimodal world model for mobile networks that learns state-action dynamics to enable unlimited-horizon counterfactual traffic simulations and optimization.
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Incident-Guided Spatiotemporal Traffic Forecasting
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
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Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
GenTTP is a generalized predictor that learns to forecast network-wide travel times and flows for arbitrary route choice distributions rather than only typical daily patterns.
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Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learning
SA-HGNN with contrastive learning improves power outage prediction by modeling spatial effects of extreme weather on infrastructure across multiple utility territories.
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Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.
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FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing
FASE pairs a spatiotemporal graph neural network and multivariate Hawkes process for crime prediction with a fairness-constrained linear program for patrol allocation, showing that allocation fairness holds in simulation but a 3.5 percentage point detection gap between minority and non-minority ZIPs