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As a vital research subject in the field of intelligent transportation systems (ITSs), traffic flow prediction using deep learning methods has attracted much attention in recent years. However, numerous existing studies mainly focus on short-term traffic flow predictions and fail to consider the influence of external factors. Effective long-term traffic flow prediction has become a challenging ...
14 de mar. de 2020 · The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom.
10 de jun. de 2022 · Traffic flow prediction plays a critical role in reducing traffic congestion in transportation systems. However, accurate traffic flow prediction becomes challenging due to the impact of complex spatio-temporal (ST) correlations and the diversity of ST correlations. When modeling complicated ST correlations, researchers usu did not take the diversity of ST correlations into consideration ...
1 de nov. de 2023 · Overview of traffic flow prediction methods. This section mainly divides traditional TF prediction methods into modelling-based and learning-based methods. Modelling-based prediction methods refer to the traditional ML methods, while learning-based prediction methods are dominated by NN and DL methods. 2.1.1. Modelling-based traffic flow prediction
1 de ene. de 2019 · In this case, deep learning models and algorithms are introduced into traffic flow prediction, including DBN, AE, CNN, and LSTM, which are categorized into two types. The first type includes the DBN and AE that extract the features of traffic flow data by reconstructing the input data layer by layer.
27 de feb. de 2017 · Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow) model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective ...
11 de dic. de 2020 · Traffic flow prediction using Deep Sedenion Networks. December 2020. Alabi Bojesomo; Panos Liatsis; Hasan Al Marzouqi; Traffic4cast2020 is the second year of NeurIPS competition where participants ...
