Mar 3, 2017 · We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the congestion state ...
A decentralized deep learning-based method where each node accurately predicts its own congestion state in realtime based on the congestion state of the ...
Mar 3, 2017 · We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the ...
Oct 5, 2017 · Deep learning methodologies have spurred numerous investigations into predicting temporal and spatial data. Convolutional neural networks ...
Mar 3, 2017 · Abstract—Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems.
This paper, therefore, proposes a scalable deep learning framework that learns traffic flow parameters as images and predicts multi-step traffic flow. The input ...
A scalable deep learning framework that learns traffic flow parameters as images and predicts multi-step traffic flow and the experimental results ...
They combine the ARIMA method and the Nonlinear Wavelet Neural Network method to predict traffic flow. When evaluating the proposed model, the results show ...
People also ask
Which algorithm is used for traffic prediction?
What are the models of traffic flow prediction?
Is traffic prediction machine learning?
In this paper, we propose a network-scale deep traffic prediction model called TrafficGAN, in which Generative Adversarial Nets (GAN) is utilized to predict ...
We develop a deep neural network model for efficient end-to-end prediction of transportation network congestion levels by using hierarchical feature extraction.