Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
A comprehensive survey on deep graph representation learning methods
IA Chikwendu, X Zhang, IO Agyemang… - Journal of Artificial …, 2023 - jair.org
There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …
representation learning aims to produce graph representation vectors to represent the …
Meta-learning approaches for learning-to-learn in deep learning: A survey
Y Tian, X Zhao, W Huang - Neurocomputing, 2022 - Elsevier
Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …
representation and understand scattered data properties. It has gained considerable …
Meta graph transformer: A novel framework for spatial–temporal traffic prediction
Accurate traffic prediction is critical for enhancing the performance of intelligent
transportation systems. The key challenge to this task is how to properly model the complex …
transportation systems. The key challenge to this task is how to properly model the complex …
Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities
V Papastefanopoulos, P Linardatos… - Smart Cities, 2023 - mdpi.com
Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …
conventional networks and services for sustainable growth, optimized resource …
MR-selection: A meta-reinforcement learning approach for zero-shot hyperspectral band selection
Band selection is an effective method to deal with the difficulties in image transmission,
storage, and processing caused by redundant and noisy bands in hyperspectral images …
storage, and processing caused by redundant and noisy bands in hyperspectral images …
Improving small objects detection using transformer
General artificial intelligence counteracts the inductive bias of an algorithm and tunes the
algorithm for out-of-distribution generalization. A conspicuous impact of the inductive bias is …
algorithm for out-of-distribution generalization. A conspicuous impact of the inductive bias is …
Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction
M Xu, TZ Qiu, J Fang, H He, H Chen - Expert Systems with Applications, 2023 - Elsevier
Forecasting the forthcoming intersection movement-based traffic volume enables adaptive
traffic control systems to dynamically respond to the fluctuation of traffic demands. In this …
traffic control systems to dynamically respond to the fluctuation of traffic demands. In this …
Advances in spatiotemporal graph neural network prediction research
Y Wang - International Journal of Digital Earth, 2023 - Taylor & Francis
Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …
ST-CRMF: compensated residual matrix factorization with spatial-temporal regularization for graph-based time series forecasting
Despite the extensive efforts, accurate traffic time series forecasting remains challenging. By
taking into account the non-linear nature of traffic in-depth, we propose a novel ST-CRMF …
taking into account the non-linear nature of traffic in-depth, we propose a novel ST-CRMF …