Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
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 …
[HTML][HTML] Urban traffic flow prediction techniques: A review
In recent decades, the development of transport infrastructure has had a great development,
although traffic problems continue to spread due to increase due to the increase in the …
although traffic problems continue to spread due to increase due to the increase in the …
Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction
Traffic flow forecasting is of great importance in intelligent transportation systems for
congestion mitigation and intelligent traffic management. Most of the existing methods …
congestion mitigation and intelligent traffic management. Most of the existing methods …
A flow feedback traffic prediction based on visual quantified features
Traffic flow prediction methods commonly rely on historical traffic data, such as traffic volume
and speed, but may not be suitable for high-capacity expressways or during peak traffic …
and speed, but may not be suitable for high-capacity expressways or during peak traffic …
Learning to dispatch for job shop scheduling via deep reinforcement learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
Bearing remaining useful life prediction using self-adaptive graph convolutional networks with self-attention mechanism
Bearings are commonly used to reduce friction between moving parts. Bearings may fail due
to lubrication failure, contamination, corrosion, and fatigue. To prevent bearing failures, it is …
to lubrication failure, contamination, corrosion, and fatigue. To prevent bearing failures, it is …
Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical
Systems) technologies, big spatiotemporal data are being generated from mobile phones …
Systems) technologies, big spatiotemporal data are being generated from mobile phones …
[HTML][HTML] RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
On the equivalence between temporal and static equivariant graph representations
This work formalizes the associational task of predicting node attribute evolution in temporal
graphs from the perspective of learning equivariant representations. We show that node …
graphs from the perspective of learning equivariant representations. We show that node …
A physics-informed transformer model for vehicle trajectory prediction on highways
Abstract Autonomous Vehicles (AVs) have made remarkable developments and are
anticipated to replace human drivers. In transitioning from human-driven vehicles to fully …
anticipated to replace human drivers. In transitioning from human-driven vehicles to fully …