Memory attention enhanced graph convolution long short‐term memory network for traffic forecasting
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22855
Abstract: In recent years, traffic forecasting has gradually attracted attention in data mining because of the increasing availability of large‐scale traffic data. However, it faces substantial challenges of complex temporal‐spatial correlations in traffic. Recent studies mainly… read more here.
Keywords: traffic forecasting; attention; traffic; graph convolution ... See more keywords
Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours
Sign Up to like & getrecommendations! Published in 2017 at "Iet Intelligent Transport Systems"
DOI: 10.1049/iet-its.2016.0263
Abstract: Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbour (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to… read more here.
Keywords: traffic; knn; term traffic; traffic forecasting ... See more keywords
Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting
Sign Up to like & getrecommendations! Published in 2022 at "Connection Science"
DOI: 10.1080/09540091.2021.2006607
Abstract: Traffic forecasting is highly challenging due to its complex spatial and temporal dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been effectively used for traffic forecasting due to its excellent performance in… read more here.
Keywords: graph convolutional; traffic forecasting; convolutional neural; traffic ... See more keywords
A Gated Dilated Causal Convolution Based Encoder-Decoder for Network Traffic Forecasting
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2019.2963449
Abstract: The accurate estimation of future network traffic is a key enabler for early warning of network degradation and automated orchestration of network resources. The long short-term memory neural network (LSTM) is a popular architecture for… read more here.
Keywords: dilated causal; traffic forecasting; gated dilated; network traffic ... See more keywords
A Communication-Efficient Federated Learning Scheme for IoT-Based Traffic Forecasting
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3132363
Abstract: Federated learning (FL) is widely adopted in traffic forecasting tasks involving large-scale IoT-enabled sensor data since its decentralization nature enables data providers’ privacy to be preserved. When employing state-of-the-art deep learning-based traffic predictors in FL… read more here.
Keywords: communication; based traffic; traffic forecasting; federated learning ... See more keywords
Road Traffic Forecasting: Recent Advances and New Challenges
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Intelligent Transportation Systems Magazine"
DOI: 10.1109/mits.2018.2806634
Abstract: Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied… read more here.
Keywords: traffic; traffic forecasting; forecasting recent; recent advances ... See more keywords
An Optimized Temporal-Spatial Gated Graph Convolution Network for Traffic Forecasting
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Intelligent Transportation Systems Magazine"
DOI: 10.1109/mits.2019.2962138
Abstract: Traffic forecasting is a challenging problem because of the irregular and complex road network in space and the dynamic and non-stationary traffic flow in time. To solve this problem, the recently proposed temporal graph convolution… read more here.
Keywords: traffic; convolution network; graph convolution; network ... See more keywords
Traffic Forecasting via Dilated Temporal Convolution With Peak-Sensitive Loss
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Intelligent Transportation Systems Magazine"
DOI: 10.1109/mits.2021.3119869
Abstract: Deep learning-based traffic forecasting methods can capture intricate spatiotemporal features in traffic data and environmental factors. However, they have unsatisfactory performance around the minority peaks and are inefficient for modeling wide-range spatial correlations. This article… read more here.
Keywords: sensitive loss; traffic forecasting; peak sensitive; loss ... See more keywords
Robust and Hierarchical Spatial Relation Analysis for Traffic Forecasting
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2022.3217054
Abstract: How to model the complex spatial-temporal relation in traffic data is an important problem for precisely predicting the future status of a city traffic system. Existing traffic forecasting methods rarely consider the traffic state trend,… read more here.
Keywords: spatial relation; traffic forecasting; traffic; robust hierarchical ... See more keywords
Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2021.3056502
Abstract: Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of trafc data along… read more here.
Keywords: temporal graph; spatial temporal; traffic forecasting; heterogeneity ... See more keywords
Graph Neural Network-Driven Traffic Forecasting for the Connected Internet of Vehicles
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Network Science and Engineering"
DOI: 10.1109/tnse.2021.3126830
Abstract: Due to great advances in wireless communication, the connected Internet of vehicles (CIoVs) has become prevalent. Naturally, internal connections among active vehicles are an indispensable factor in traffic forecasting. Although many related research studies have… read more here.
Keywords: network; traffic forecasting; traffic; internet vehicles ... See more keywords