Particularly, we build a LSTM model with different dimensions of historical data, and then make prediction almost in real time for current traffic flows. We ...
In this paper, we aim to predict traffic flow with big traffic data in urban cities. Our intuition is that although the traffic information is influenced by ...
This paper builds a LSTM model with different dimensions of historical data, and makes prediction almost in real time for current traffic flows, ...
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 ...
Dec 20, 2024 · This study focuses on the development of an LSTM-based predictive model that uses historical traffic data to predict traffic flow two hours into ...
Aug 30, 2024 · The research concludes that by using this predictive type of LSTM networks, it can full-fill a great role in solving the urban mobility problem ...
In this paper, the short-term traffic flow prediction model is constructed by using the short-term and short-term memory network, and the modal aliasing problem ...
Jul 3, 2024 · By using LSTM and GRU models,. we explore more features and multi-layer models to increase the accuracy of traffic flow prediction. ... traffic ...
Jan 2, 2024 · We compare the prediction accuracy of LSTM and GRU models in urban traffic flow prediction. The data collected in this study are divided into three categories.
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In this paper, we explore the application of Long Short-Term Memory Networks (LSTMs) in short-term traffic flow prediction.