Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
A survey of urban visual analytics: Advances and future directions
Developing effective visual analytics systems demands care in characterization of domain
problems and integration of visualization techniques and computational models. Urban …
problems and integration of visualization techniques and computational models. Urban …
Pyramid: Enabling hierarchical neural networks with edge computing
Machine learning (ML) is powering a rapidly-increasing number of web applications. As a
crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model …
crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model …
Urban regional function guided traffic flow prediction
The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis,
which has recently gained increasing interest. In addition to spatial-temporal correlations …
which has recently gained increasing interest. In addition to spatial-temporal correlations …
Spatial-temporal hypergraph self-supervised learning for crime prediction
Crime has become a major concern in many cities, which calls for the rising demand for
timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the …
timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the …
Multi-view dynamic graph convolution neural network for traffic flow prediction
X Huang, Y Ye, X Yang, L Xiong - Expert Systems with Applications, 2023 - Elsevier
The rapid urbanization and continuous improvement of road traffic equipment result in
massive daily production of traffic data. These data contain the long-term evolution of traffic …
massive daily production of traffic data. These data contain the long-term evolution of traffic …
Spatio-temporal hierarchical MLP network for traffic forecasting
Traffic forecasting is an indispensable part of intelligent transportation systems. However,
existing methods suffer from limited capability in capturing hierarchical temporal …
existing methods suffer from limited capability in capturing hierarchical temporal …
When do contrastive learning signals help spatio-temporal graph forecasting?
Deep learning models are modern tools for spatio-temporal graph (STG) forecasting.
Though successful, we argue that data scarcity is a key factor limiting their recent …
Though successful, we argue that data scarcity is a key factor limiting their recent …
Multi-mode dynamic residual graph convolution network for traffic flow prediction
X Huang, Y Ye, W Ding, X Yang, L Xiong - Information Sciences, 2022 - Elsevier
Urban traffic congestion is not only an important cause of traffic accidents, but also a major
hinder to urban development. By learning the historical traffic flow data, we can forecast the …
hinder to urban development. By learning the historical traffic flow data, we can forecast the …
Crowd flow prediction for irregular regions with semantic graph attention network
It is essential to predict crowd flow precisely in a city, which is practically partitioned into
irregular regions based on road networks and functionality. However, prior works mainly …
irregular regions based on road networks and functionality. However, prior works mainly …