An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic

By Jinjun Tang, Fang Liu, Yajie Zou, Weibin Zhang, Yinhai Wang. IEEE transactions on intelligent transportation systems (Print), 2017

334 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/ca1d7b5637e9bd72e347731c820ed3fdd719280a

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. Weibin Zhang
2. Haitao Yuan
3. Guoliang Li
4. Yinghao Yu
5. Feng Shu
6. Yong Qi
7. Yinhai Wang

In Control and Systems Engineering

1. Haitao Yuan
2. Guoliang Li
3. Weibin Zhang
4. Yinhai Wang

In Transportation

1. Haitao Yuan
2. Guoliang Li
3. Yinghao Yu
4. Weibin Zhang
5. Feng Shu
6. Yong Qi
7. Yinhai Wang

Citations in the corpus

• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, methodology): “[169] propose an evolving fuzzy neural network with two proposed learning processes, where the first is to cluster inputs and the second is to optimize parameters in the Takagi–Sugeno-type fuzzy rules.”
• Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, background): “In general, spatio-temporal feature is one of the most important characteristic of traffic flow data (Tang et al. 2017).”