Graph neural networks in IoT: A survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
Spatio-temporal meta-graph learning for traffic forecasting
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 …
significant research topic in AI community. To address the spatio-temporal heterogeneity …
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 …
Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting
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 the …
forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …
Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting
Q Ren, Y Li, Y Liu - Expert Systems with Applications, 2023 - Elsevier
Abstract Recently, Temporal Convolution Networks (TCNs) and Graph Convolution Network
(GCN) have been developed for traffic forecasting and obtained promising results as their …
(GCN) have been developed for traffic forecasting and obtained promising results as their …
Hybrid deep learning models for traffic prediction in large-scale road networks
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …
enabling advanced transportation management and services to address worsening traffic …
Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction
Deep learning models have emerged as a promising way for traffic prediction. However, the
requirement for large amounts of training data remains a significant issue for achieving well …
requirement for large amounts of training data remains a significant issue for achieving well …
Transferable graph structure learning for graph-based traffic forecasting across cities
Graph-based deep learning models are powerful in modeling spatio-temporal graphs for
traffic forecasting. In practice, accurate forecasting models rely on sufficient traffic data …
traffic forecasting. In practice, accurate forecasting models rely on sufficient traffic data …
Selective cross-city transfer learning for traffic prediction via source city region re-weighting
Deep learning models have been demonstrated powerful in modeling complex spatio-
temporal data for traffic prediction. In practice, effective deep traffic prediction models rely on …
temporal data for traffic prediction. In practice, effective deep traffic prediction models rely on …
Difftraj: Generating gps trajectory with diffusion probabilistic model
Pervasive integration of GPS-enabled devices and data acquisition technologies has led to
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …