Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification

By Jianhua Guo, Wei Huang, Billy M. Williams. 2014

608 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b08039f68e7189942cc30a97ef0c70ac723b6426

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. Zhiyong Cui
3. Ruimin Ke
4. Weibin Zhang
5. Yunpeng Wang
6. Xiaolei Ma
7. Yinghao Yu
8. Li Li

In Control and Systems Engineering

1. Yinhai Wang
2. Zhiyong Cui
3. Ruimin Ke
4. Weibin Zhang
5. Li Li
6. Xuegang (Jeff) Ban
7. Qiangqiang Guo
8. Hai Yang

In Transportation

1. Yunpeng Wang
2. Xiaolei Ma
3. Yinhai Wang
4. Jintao Ke
5. Yinghao Yu
6. Weibin Zhang
7. Zhiyong Cui
8. Li Li

Citations in the corpus

• Urban traffic signal control with connected and automated vehicles: A survey (2019, methodology): “Other stochastic filtering techniques such as Kalman filter (Guo et al., 2014; Tampère and Immers, 2007), particle filter (Gustafsson et al., 2002; Marinica et al., 2013), and other machine learning based approaches (Lv et al., 2015; Rompis et al., 2017) have been applied to help estimate queue…”
• Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks (2017, background): “downstream location information and yield high accuracy [16]. Motivated by the superior capability to cast the regression problem of a KF, numerous KF-based traffic prediction studies began to emerge [17, 18, 19]. S.H. Hosseini et al. applied an adaptive neuro fuzzy inference system (ANFIS) based on”
• Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach (2017, background): “Although little direct experience suggests solutions to these three depen- dences in short-term passenger demand forecasting, studies on traffic speed/volume prediction and rainfall nowcasting provide valuable insights (Ghosh et al., 2009; Huang and Sadek, 2009; Guo et al., 2014; Wang et al., 2014).”
• Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, methodology): “The Kalman filtering method is a typical traffic forecasting parametric model, and can cast the regression problem in a state space by minimizing the variance to obtain the optimal solution (Okutani and Stephanedes 1984; Yang and Zhu1999; Guo, Huang, and Williams 2014).”
• Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018, methodology): “Many classical baseline models used in traffic forecasting problems, like ARIMA [2, 23] Support Vector Regression (SVR) [37], Kalman filter [35].”