MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
Proceedings of the AAAI Conference on Artificial Intelligence 34, 3187–3194
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A survey synthesizing LLM and MM-LLM uses in transportation operations, mobility services, and decision support while noting challenges like data heterogeneity and real-time needs.
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MoGERNN: An Inductive Traffic Predictor for Unobserved Locations
MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.