Abstract: Traffic flow forecasting is a popular research topic of Intelligent Transportation Systems (ITS). Some forecasting models have been developed, ...
Jul 11, 2024 · First, the belief rule base (BRB) is used for data fusion to obtain new traffic flow data, then the recurrent neural network (RNN) and graph ...
Support vector machines (SVM) is used to forecast traffic flow. Bayesian inference is used to fix the SVMpsilas kernel parameters to improve the regression ...
Apr 7, 2024 · This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion ...
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Oct 19, 2020 · This paper proposes a new traffic flow prediction method based on RNN-GCN and BRB. First, the belief rule base (BRB) is used for data fusion to obtain new ...
Nov 14, 2023 · Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively.
Dec 21, 2022 · This paper reviews recent short-term traffic forecasting models and summarizes them based on four broad design parameters.
Sep 11, 2024 · This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting.
Jan 25, 2022 · A novel deep learning traffic flow forecasting framework is proposed in this paper, termed as Ensemble Attention based Graph Time Convolutional Networks ( ...
In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural ...