Enhancing the robustness via adversarial learning and joint spatial-temporal embeddings in traffic forecasting
Traffic forecasting is an essential problem in urban planning and computing. The complex
dynamic spatial-temporal dependencies among traffic objects (eg, sensors and road …
dynamic spatial-temporal dependencies among traffic objects (eg, sensors and road …
PFNet: Large-Scale Traffic Forecasting With Progressive Spatio-Temporal Fusion
C Wang, K Zuo, S Zhang, H Lei, P Hu… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Traffic flow forecasting on a large-scale sensor network is of great practical significance for
policy decision-making, urban management, and transport planning. Recently, several …
policy decision-making, urban management, and transport planning. Recently, several …
Statistical deep learning for spatial and spatio-temporal data
CK Wikle, A Zammit-Mangion - arXiv preprint arXiv:2206.02218, 2022 - arxiv.org
Deep neural network models have become ubiquitous in recent years, and have been
applied to nearly all areas of science, engineering, and industry. These models are …
applied to nearly all areas of science, engineering, and industry. These models are …
Spatial-temporal graph boosting networks: Enhancing spatial-temporal graph neural networks via gradient boosting
Spatial-temporal graph neural networks (STGNNs) are promising in solving real-world
spatial-temporal forecasting problems. Recognizing the inherent sequential relationship of …
spatial-temporal forecasting problems. Recognizing the inherent sequential relationship of …
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
Spectral-temporal graph neural network is a promising abstraction underlying most time
series forecasting models that are based on graph neural networks (GNNs). However, more …
series forecasting models that are based on graph neural networks (GNNs). However, more …
Urban mobility analytics: A deep spatial–temporal product neural network for traveler attributes inference
This study examines the potential of using smart card data in public transit systems to infer
attributes of travelers, thereby facilitating a more user-centered public transport service …
attributes of travelers, thereby facilitating a more user-centered public transport service …
Autost: Towards the universal modeling of spatio-temporal sequences
The analysis of spatio-temporal sequences plays an important role in many real-world
applications, demanding a high model capacity to capture the interdependence among …
applications, demanding a high model capacity to capture the interdependence among …
Jointly modeling spatio–temporal dependencies and daily flow correlations for crowd flow prediction
Crowd flow prediction is a vital problem for an intelligent transportation system construction
in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it …
in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it …
Predicting citywide crowd dynamics at big events: A deep learning system
Event crowd management has been a significant research topic with high social impact.
When some big events happen such as an earthquake, typhoon, and national festival, crowd …
When some big events happen such as an earthquake, typhoon, and national festival, crowd …
[HTML][HTML] Site selection via learning graph convolutional neural networks: A case study of Singapore
Selection of store sites is a common but challenging task in business practices. Picking the
most desirable location for a future store is crucial for attracting customers and becoming …
most desirable location for a future store is crucial for attracting customers and becoming …