8 papers
Decorrelating the Future: Joint Frequency Domain Learning for Spatio-temporal Forecasting
Zepu Wang, Bowen Liao, Jeff +1
Standard direct forecasting models typically rely on point-wise objectives such as Mean Squared Error, which fail to capture the complex spatio-temporal dependencies inherent in gr…
Event-CausNet: Unlocking Causal Knowledge from Text with Large Language Models for Reliable Spatio-Temporal Forecasting
Luyao Niu, Zepu Wang, Shuyi Guan +2
While spatio-temporal Graph Neural Networks (GNNs) excel at modeling recurring traffic patterns, their reliability plummets during non-recurring events like accidents. This failure…
Domain Adaptation Framework for Turning Movement Count Estimation with Limited Data
Xiaobo Ma, Hyunsoo Noh, Ryan Hatch +2
Urban transportation networks are vital for the efficient movement of people and goods, necessitating effective traffic management and planning. An integral part of traffic managem…
A Survey on Diffusion Models for Anomaly Detection
Jing Liu, Zhenchao Ma, Zepu Wang +7
Diffusion models (DMs) have emerged as a powerful class of generative AI models, showing remarkable potential in anomaly detection (AD) tasks across various domains, such as cybers…
Unlocking the Power of LSTM for Long Term Time Series Forecasting
Yaxuan Kong, Zepu Wang, Yuqi Nie +5
Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF)…
Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks
Zijian Zhang, Yujie Sun, Zepu Wang +5
Mobility analysis is a crucial element in the research area of transportation systems. Forecasting traffic information offers a viable solution to address the conflict between incr…