Travel time prediction with LSTM neural network

By Yanjie Duan, Yisheng Lv, Feiyue Wang. 2016

331 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

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Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, neutral): “ popular form of RNNs that are specically introduced to alleviate the limitations of vanilla RNNs, have been successfully applied in several time series forecasting applications (Tian and Pan, 2015; Duan et al., 2016; Lee et al., 2015). However, despite the architectural suitability and increasing d”
Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station (2020, background): “There are several evaluation metrics which can be used to calculate the loss in a neural network such as but not limited to mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), quadratic cost, cross-entry cost, exponential cost, Hellinger distance, and Kullback–Leible”
High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, methodology): “With the capability of capturing temporal dependencies, the recurrent NN or its variants, like LSTM [12] and GRU [13], was widely adopted as a component of a traffic forecasting model to forecast traffic speed [8], travel time [36], and traffic flow [37].”
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2018, methodology): “[28] used LSTM RNN for travel time pre-”
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018, methodology): “Traffic speed prediction at one location normally uses a sequence of speed values with 𝑛 historical time steps as the input data [2, 22, 23], which can be represented by a vector, (1) But the traffic speed at one location may be influenced by the speeds of nearby locations or even locations…”
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, methodology): “ Deep learning models, as a branch of machine learning models, become popular and rapidly be adopted in the trac forecasting area. Most of the newly proposed trac forecasting modelsMa et al. (2015);Duan et al. (2016);Chen et al. (2016);Zhao et al. (2017);Wu and Tan(2016);Song et al. (2016);Yu et al.”
An LSTM network for highway trajectory prediction (2017, background): “le to keep a memory of previous inputs, LSTMs are considered particularly efﬁcient for time series prediction [21] and have been widely used in the past few years for pedestrian trajectory prediction [16], [17] or to predict vehicle destinations at an intersection [11], [22]. Our main contribution i”