Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks

By J. V. Lint, Serge P. Hoogendoorn, Henk J. van Zuylen. 2002

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f77782841495de85fed895d51b1560a202d4dd58

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yinhai Wang
2. Zhiyong Cui
3. Ruimin Ke
4. Yiannis Kamarianakis
5. Poulicos Prastacos
6. Kristian C. Henrickson
7. Ling Zhao
8. Chao Zhang

In Building and Construction

1. Yinhai Wang
2. Ruimin Ke
3. Zhiyong Cui
4. Yiannis Kamarianakis
5. Poulicos Prastacos
6. Kristian C. Henrickson
7. Eleni I. Vlahogianni
8. John C. Golias

In Signal Processing

1. Yinhai Wang
2. Zhiyong Cui
3. Ruimin Ke
4. Yiannis Kamarianakis
5. Poulicos Prastacos
6. Kristian C. Henrickson
7. Ling Zhao
8. Tao Lin

Citations in the corpus

• A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting (2020, background): “Recurrent neural networks (RNNs), long short-term memory (LSTM) [13], and gated recurrent units (GRUs)[7] have been successfully utilized in traffic forecasting because they can use self-circulation mechanism and model temporal dependence [20, 25].”
• T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2018, background): “In addition, since the recurrent neural network (RNN) and its variants have long short-term memory (LSTM) and the gated recurrent unit (GRU) can effectively use the self-circulation mechanism, they can learn temporal dependence well and achieve better prediction results [13], [41].”
• Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018, methodology): “Ever since the precursory study of utilizing NN into the traffic prediction problem was proposed [39], many NN-based methods, like feed forward NN [41], fuzzy NN [40], recurrent NN (RNN) [42], and hybrid NN [25], are adopted for traffic forecasting problems.”
• Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches (2003, methodology): “ARIMA models have been applied to the UTCS (24) and to freeway volume forecasting (5).”
• Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, methodology): “Ever since the precursory study [25] using the feed-forward NN for vehicle travel time estimation was proposed, many other NN-based models, including fuzzy NN [26], recurrent NN [6], convolution NN [13], deep belief networks [7], and autoencoders [8], and combinations of these”
• Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting (2017, background): “We note interesting observations from the trained neural network.”
• Short‐term traffic forecasting: Overview of objectives and methods (2004, background): “Some examples of prediction algorithms developed in freeways and highways aimed at operating as traveller information systems are the work of Park and Rilett (1998), Dia (2001), Zhang and Rice (2003) and Van Lint et al. (2002).”
• Space-time modeling of traffic flow (2002, unknown): “Keywords: ARIMA; Time series; Network”
• High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, methodology): “In addition, as introduced in the aforementioned subsection, when applying RNN-based structures, the label of a time step in a traffic state sequence is the input of the next step.”
• Accurate freeway travel time prediction with state-space neural networks under missing data (2005, background): “These models are referred to as instantaneous travel time predictors, see e.g. Zhang and Rice (2003) and Van Toorenburg (1998)—in Dutch.”