4 papers
PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting
Tongtong Zhang, Zhiyong Cui, Bingzhang Wang +4
In urban computing, precise and swift forecasting of multivariate time series data from traffic networks is crucial. This data incorporates additional spatial contexts such as sens…
HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting
Shaohan Yu, Pan Deng, Yu Zhao +2
Achieving both accurate and consistent predictive performance across spatial nodes is crucial for ensuring the validity and reliability of outcomes in fair spatial-temporal forecas…
Causal conditional hidden Markov model for multimodal traffic prediction
Yu Zhao, Pan Deng, Junting Liu +2
Multimodal traffic flow can reflect the health of the transportation system, and its prediction is crucial to urban traffic management. Recent works overemphasize spatio-temporal c…
Spatio-temporal neural structural causal models for bike flow prediction
Pan Deng, Yu Zhao, Junting Liu +2
As a representative of public transportation, the fundamental issue of managing bike-sharing systems is bike flow prediction. Recent methods overemphasize the spatio-temporal corre…