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1 de nov. de 2023 · According to Fig. 2, out of the 21 documents identified in the literature as doing prediction of traffic flow, 76% used deep learning models and less than 40% used parametric models. Only one study (around 5%) was identified as using genetic programming to predict traffic flow.
1 de ene. de 2014 · In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to ...
1 de jun. de 2017 · Deep learning for traffic flow prediction. Let x t + h t be the forecast of traffic flow speeds at time t + h, given measurements up to time t. Our deep learning traffic architecture looks like y ( x): = x t + 40 t = x 1, t + 40 ⋮ x n, t + 40. To model traffic flow data x t = ( x t - k, …, x t) we use predictors x given by x t = vec x 1, t ...
Abstract: Deep neural networks are being increasingly used for short-term traffic flow prediction, which can be generally categorized as convolutional (CNNs) or graph neural networks (GNNs). CNNs are preferable for region-wise traffic prediction by taking advantage of localized spatial correlations, whilst GNNs achieves better performance for graph-structured traffic data.
Airport traffic flow prediction is a fundamental research topic in the field of air traffic flow management. Most existing works focus on the single airport traffic flow prediction with temporal dynamics but fail to consider the influence of the topological airport network. In this paper, a novel deep learning-based framework, called airport traffic flow prediction network (ATFPNet), is ...
1 de oct. de 2016 · Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through a city. We design an ...
27 de ene. de 2022 · Experiments with the KNN model demonstrated over 90 percent accuracy of short-term traffic flow prediction. Deep learning approach Deep learning (DL) methods have proved highly effective in predicting road traffic in comparison to ML or statistical techniques, consistently showing about 90 percent forecasting accuracy and higher.
