Apr 30, 2020 · In this paper, we propose (i) an efficient and inexpensive city-wide data acquisition scheme by taking a snapshot of traffic congestion map from an open-source ...
Oct 22, 2024 · Ranjan et al. [128] proposed a hybrid neural network model combining the CNN, LSTM, and transpose CNN to predict city-wide traffic congestion.
An efficient and inexpensive city-wide data acquisition scheme by taking a snapshot of traffic congestion map from an open-source online web service; ...
In this paper, we propose (i) an efficient and inexpensive city-wide data acquisition scheme by taking a snapshot of traffic congestion map from an open-source ...
This study proposes hybrid neural network algorithms such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) network for short term ...
Missing: Transpose | Show results with:Transpose
In this paper we present a hybrid model, combining a Convolutional Neural Network and a Bidirectional Long–Short-Term Memory network, and apply it to long-term ...
Missing: Wide Transpose
A traffic speed prediction model based on the convolutional neural network (CNN), stacked bidirectional LSTM (BiLSTM), and unidirectional lSTM neural network ...
Missing: Transpose | Show results with:Transpose
People also ask
What is the difference between LSTM and CNN-LSTM?
What is CNN-LSTM for forecasting?
What is the CNN-LSTM model structure?
In this study, we propose a hybrid deep neural network algorithm based on High-Resolution Network (HRNet) and ConvLSTM decoder for 10, 30, and 60-min traffic ...
This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion ...
Aug 27, 2020 · A MAPE rate of 22% was achieved. Zhang [10] proposed a short-term traffic prediction model based on a convolutional neural network (CNN) deep ...