Accurate freeway travel time prediction with state-space neural networks under missing data

By J. V. Lint, Serge P. Hoogendoorn, Henk J. van Zuylen. 2005

471 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/ceb94f539ddc3be17b4f0d0b2a8cd5dd751f9e80

How researchers see this paper

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

In Building and Construction

1. Yinhai Wang
2. Weibin Zhang
3. Feiyue Wang
4. Afshin Abadi
5. Baocai Yin
6. Mu-Chen Chen
7. Yu Wei
8. Yanjie Duan

In Control and Systems Engineering

1. Yinhai Wang
2. Weibin Zhang
3. Feiyue Wang
4. Tao Tang
5. Bin Ning
6. Xin Xu
7. Mu-Chen Chen
8. Yu Wei

In Transportation

1. Yinhai Wang
2. Weibin Zhang
3. Feiyue Wang
4. Afshin Abadi
5. Tao Tang
6. Bin Ning
7. Baocai Yin
8. Mu-Chen Chen

Citations in the corpus

• Travel time prediction with LSTM neural network (2016, background): “The state-space neural network (SSNN) [13] with a topology of recurrent neural network (RNN) connects each hidden unit with the traffic flow and average speed data collected from one of the detectors on the concerned section and the hidden layer in previous time period.”
• Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2012, background): “…radial basis function neural networks (Zheng et al., 2006), dynamic neural networks (Chen and Grant-Muller, 2001), state-space neural networks (Van Lint et al., 2005), and the support vector machine for regression (Vanajaksi and Rilett, 2007; Castro-Neto et al., 2009), etc. Multilayer perceptron…”
• Data-Driven Intelligent Transportation Systems: A Survey (2011, background): “[12] focused on a specific ITS application, i.”
• Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction (2021, methodology): “Thus the spatial relations were modeled by matrices or nonlinear functions [26], for example, CNN [37], [39].”
• An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, background): “Recently, some improve algorithm, such as state-space neural network model [13], Long Short-Term Memory Neural Network (LSTM) [14] have demonstrated their superiority to traditional ones for high computational efficiency and performance.”
• Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data (2015, background): “Short-term forecasting models include nonlinear models such as neural network models [4]–[8] and linear models such as Kalman filters [9]–[13] and autoregressive integrated moving average (ARIMA) models [14]–[17].”
• Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, methodology): “As a data modeling tool, it has also been adopted in ITS such as traffic flow prediction [66], travel time prediction [67], traffic accident”