Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Largest: A benchmark dataset for large-scale traffic forecasting
Road traffic forecasting plays a critical role in smart city initiatives and has experienced
significant advancements thanks to the power of deep learning in capturing non-linear …
significant advancements thanks to the power of deep learning in capturing non-linear …
Deciphering spatio-temporal graph forecasting: A causal lens and treatment
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis
Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex
systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting …
systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting …
Cross-city few-shot traffic forecasting via traffic pattern bank
Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing
deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices …
deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices …
St-mlp: A cascaded spatio-temporal linear framework with channel-independence strategy for traffic forecasting
The criticality of prompt and precise traffic forecasting in optimizing traffic flow management
in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus. Spatio …
in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus. Spatio …
BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks
Spatio-Temporal Graph Neural Network (STGNN) has been used as a common workhorse
for traffic forecasting. However, most of them require prohibitive quadratic computational …
for traffic forecasting. However, most of them require prohibitive quadratic computational …
A lightweight multi-layer perceptron for efficient multivariate time series forecasting
Efficient and effective multivariate time series (MTS) forecasting is critical for real-world
applications, such as traffic management and energy dispatching. Most of the current deep …
applications, such as traffic management and energy dispatching. Most of the current deep …
Contextualizing MLP-mixers spatiotemporally for urban traffic data forecast at scale
Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive
advanced techniques have been designed to capture these structures for effective …
advanced techniques have been designed to capture these structures for effective …
Dual-track spatio-temporal learning for urban flow prediction with adaptive normalization
Robust urban flow prediction is crucial for transportation planning and management in urban
areas. Although recent advances in modeling spatio-temporal correlations have shown …
areas. Although recent advances in modeling spatio-temporal correlations have shown …