When intelligent transportation systems sensing meets edge computing: Vision and challenges
The widespread use of mobile devices and sensors has motivated data-driven applications
that can leverage the power of big data to benefit many aspects of our daily life, such as …
that can leverage the power of big data to benefit many aspects of our daily life, such as …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
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 …
Spatiotemporal attention-based graph convolution network for segment-level traffic prediction
Traffic prediction, as a core component of intelligent transportation systems (ITS), has been
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …
Two-stream multi-channel convolutional neural network for multi-lane traffic speed prediction considering traffic volume impact
Traffic speed prediction is a critically important component of intelligent transportation
systems. Recently, with the rapid development of deep learning and transportation data …
systems. Recently, with the rapid development of deep learning and transportation data …
Graph attention temporal convolutional network for traffic speed forecasting on road networks
Traffic speed forecasting plays an increasingly essential role in successful intelligent
transportation systems. However, this still remains a challenging task when the accuracy …
transportation systems. However, this still remains a challenging task when the accuracy …
Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response
In the current study, we present an application to the class of deep learning known as the
Physics Informed Neural Networks (PINNs), more specifically we develop a new implicit …
Physics Informed Neural Networks (PINNs), more specifically we develop a new implicit …
Recurrent and convolutional neural networks in structural dynamics: a modified attention steered encoder–decoder architecture versus LSTM versus GRU versus TCN …
SB Tandale, M Stoffel - Computational Mechanics, 2023 - Springer
The aim of the present study is to analyse and predict the structural deformations occurring
during shock tube experiments with a series of recurrent and temporal convolutional neural …
during shock tube experiments with a series of recurrent and temporal convolutional neural …
Traffic demand prediction based on dynamic transition convolutional neural network
Precise traffic demand prediction could help government and enterprises make better
management and operation decisions by providing them with data-driven insights. However …
management and operation decisions by providing them with data-driven insights. However …