154 citations · 342 across the 29 of their papers we have counts for
30 papers
Frequency-Domain Multi-Modality Transportation Modeling
Jiewen Deng, Hangchen Liu, Junchen Li +2
Multi-modality transportation refers to urban systems composed of multiple transportation modes, such as traffic flow and public transit, whose dynamics are coupled by shared tempo…
Continuous Domain Generalization
Zekun Cai, Yiheng Yao, Guangji Bai +4
Real-world data distributions often shift continuously across multiple latent factors such as time, geography, and socioeconomic contexts. However, existing domain generalization a…
CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events
Xiaojie Yang, Hangli Ge, Jiawei Wang +4
Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these p…
Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting
Hongjun Wang, Jiyuan Chen, Lingyu Zhang +2
Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. How…
STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture comple…
Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Spatiotemporal neural networks have shown great promise in urban scenarios by effectively capturing temporal and spatial correlations. However, urban environments are constantly ev…
Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting
Hongjun Wang, Jiyuan Chen, Yinqiang Zheng +1
Climate change-driven floods demand advanced forecasting models, yet Graph Neural Networks (GNNs) underutilize river network topology due to tree-like structures causing over-squas…
Robust Traffic Forecasting against Spatial Shift over Years
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both t…
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Du Yin, Jinliang Deng, Shuang Ao +6
Training models on spatio-temporal (ST) data poses an open problem due to the complicated and diverse nature of the data itself, and it is challenging to ensure the model's perform…
Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning
Jiewen Deng, Renhe Jiang, Jiaqi Zhang +1
Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities, which is prevalent in monitoring systems, encompassing diverse tr…
Continuous Temporal Domain Generalization
Zekun Cai, Guangji Bai, Renhe Jiang +2
Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions. Traditional TDG approaches typically focus o…
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
Jiawei Wang, Renhe Jiang, Chuang Yang +5
This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome…
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
Haotian Gao, Renhe Jiang, Zheng Dong +3
Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather. Accurate prediction of spatiotemporal series remains challeng…
Hyper-Relational Knowledge Graph Neural Network for Next POI
Jixiao Zhang, Yongkang Li, Ruotong Zou +3
With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and…
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
Xiaohang Xu, Toyotaro Suzumura, Jiawei Yong +5
Next Point-of-Interest (POI) recommendation plays a crucial role in urban mobility applications. Recently, POI recommendation models based on Graph Neural Networks (GNN) have been…
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Zekun Cai, Renhe Jiang, Xinyu Yang +5
Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city. However, with the dramat…
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
Hangchen Liu, Zheng Dong, Renhe Jiang +4
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing th…
Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility
Qi Cao, Renhe Jiang, Chuang Yang +3
Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic m…
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
Jiewen Deng, Jinliang Deng, Renhe Jiang +1
Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems…
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang +4
Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many real-world applications. The core issue in MTS forecasting is how to effectively model com…
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Renhe Jiang, Zhaonan Wang, Jiawei Yong +6
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity…
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Hongjun Wang, Jiyuan Chen, Tong Pan +7
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, the…
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Renhe Jiang, Zhaonan Wang, Jiawei Yong +6
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity…
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin
Zipei Fan, Xiaojie Yang, Wei Yuan +4
Knowing "what is happening" and "what will happen" of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that m…