Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction

By Youngseon Jeong, Young-Ji Byon, Manoel Castro-Neto, Said M. Easa. IEEE transactions on intelligent transportation systems (Print), 2013

306 citations recorded by the source corpus.

Subfields: Building and Construction, Environmental Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/109f6c2ea18058cca7b9cc734b30dea6dd64cac0

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. Shengna Guo
2. Youfang Lin
3. Huaiyu Wan
4. Feiyue Wang
5. Wenwen Kang
6. Zheng Fang
7. Guojie Song
8. Chao Song

In Transportation

1. Shengna Guo
2. Youfang Lin
3. Huaiyu Wan
4. Feiyue Wang
5. Wenwen Kang
6. Zheng Fang
7. Guojie Song
8. Chao Song

Citations in the corpus

• Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting (2021, methodology): “Classic methods, including autoregressive integrated moving average (ARIMA), k-nearest neighbors algorithm (kNN), and support vector machine (SVM), can only take temporal information into account, without considering spatial features.[14, 28, 29].”
• Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (2019, methodology): “Machine learning meth-ods such as KNN (Van Lint and Van Hinsbergen 2012) and SVM (Jeong et al. 2013) Motivated by the studies mentioned above, considering the graph structure of the traffic network and the dynamic spatio-temporal patterns of the traffic data, we simultaneously employ graph…”
• Traffic Flow Prediction With Big Data: A Deep Learning Approach (2015, methodology): “An online learning weighted support vector regression (SVR) was presented in [24] for short-term traffic flow predictions.”