115 citations · 295 across the 20 of their papers we have counts for
15 papers · 1 filter
GeoFlow: Geo-Aware Modeling of Inter-Area Relationships in Origin-Destination Flow Prediction and Generation
Zherui Huang, Guanjie Zheng, Hao Xue +1
Origin-destination (OD) flow modeling underpins urban planning and mobility analysis, but prevailing graph-based methods often neglect salient geographic attributes, limiting their…
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
Arian Prabowo, Kaixuan Chen, Hao Xue +2
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment. However, this assumption bec…
Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
Arian Prabowo, Kaixuan Chen, Hao Xue +2
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. Thi…
Message Passing Neural Networks for Traffic Forecasting
Arian Prabowo, Hao Xue, Wei Shao +2
A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location. Tr…
Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)
Arian Prabowo, Hao Xue, Wei Shao +2
New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads)…
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study
Jason Liu, Shohreh Deldari, Hao Xue +2
In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several ch…