Machine learning techniques are now integral to the advancement of intelligent urban
services, playing a crucial role in elevating the efficiency, sustainability, and livability of …

Urban spatio-temporal prediction is crucial for informed decision-making, such as
transportation management, resource optimization, and urban planning. Although pretrained …

With the increasing demands of training graph neural networks (GNNs) on large-scale
graphs, graph data condensation has emerged as a critical technique to relieve the storage …

Traffic forecasting is crucial for intelligent transportation systems (ITS), aiding in efficient
resource allocation and effective traffic control. However, its effectiveness often relies heavily …

Traffic forecasting is a crucial task for enhancing the quality and efficiency of intelligent
transportation systems. In recent years, several neural networks have been proposed to …

Recent research has shown that some network traffic flow patterns are similar across
multiple traffic regions. Identifying and transferring these domain-invariant features can …

The field of Intelligent Transportation Systems (ITS) relies on accurate traffic forecasting to
enable various downstream applications. However, developing cities often face challenges …

Time series data has been demonstrated to be crucial in various research fields. The
management of large quantities of time series data presents challenges in terms of deep …

Spatio-temporal graph neural networks have demonstrated efficacy in capturing complex
dependencies for urban computing tasks such as forecasting and kriging. However, their …

In recent years, significant progress has been made in multivariate time series forecasting
using Linear-based, Transformer-based, and Convolution-based models. However, these …