Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
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 …
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 …
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 …
Ising-traffic: Using ising machine learning to predict traffic congestion under uncertainty
This paper addresses the challenges in accurate and real-time traffic congestion prediction
under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction …
under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction …
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 …
Multi-view dynamic graph convolution neural network for traffic flow prediction
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 …
GPT-ST: generative pre-training of spatio-temporal graph neural networks
In recent years, there has been a rapid development of spatio-temporal prediction
techniques in response to the increasing demands of traffic management and travel …
techniques in response to the increasing demands of traffic management and travel …
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 …