Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model

Article Properties
Cite
Zhang, Xijun, and Qirui Zhang. “Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model”. Computer Modeling in Engineering &Amp; Sciences, vol. 125, no. 1, 2020, pp. 95-109, https://doi.org/10.32604/cmes.2020.011013.
Zhang, X., & Zhang, Q. (2020). Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model. Computer Modeling in Engineering &Amp; Sciences, 125(1), 95-109. https://doi.org/10.32604/cmes.2020.011013
Zhang, Xijun, and Qirui Zhang. “Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model”. Computer Modeling in Engineering &Amp; Sciences 125, no. 1 (2020): 95-109. https://doi.org/10.32604/cmes.2020.011013.
Zhang X, Zhang Q. Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model. Computer Modeling in Engineering & Sciences. 2020;125(1):95-109.
Citations
Title Journal Journal Categories Citations Publication Date
DTM-GCN: A traffic flow prediction model based on dynamic graph convolutional network Multimedia Tools and Applications
  • Science: Science (General): Cybernetics: Information theory
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 2024
Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach Journal of Data Analysis and Information Processing 2024
Extreme gradient boosting algorithm based urban daily traffic index prediction model: a case study of Beijing, China Digital Transportation and Safety 2023
A Combined Prediction Model Composed of the GM (1,1) Model and the BP Neural Network for Major Road Traffic Accidents in China

 Mathematical Problems in Engineering
  • Technology: Engineering (General). Civil engineering (General)
  • Science: Mathematics
  • Science: Mathematics
  • Technology: Engineering (General). Civil engineering (General)
  • Technology: Engineering (General). Civil engineering (General)
 2022
A Hybrid Fuzzy Dynamic Grammar Graph Attention Network for Traffic Flow Prediction SSRN Electronic Journal 2022
Citations Analysis
Category Category Repetition
Technology: Engineering (General). Civil engineering (General)3
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks2
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software2
Science: Biology (General)1
Science: Physics1
Science: Chemistry1
Science: Chemistry: General. Including alchemy1
Technology: Technology (General): Industrial engineering. Management engineering1
Technology: Chemical technology1
Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials1
Technology: Mechanical engineering and machinery1
Science: Science (General): Cybernetics: Information theory1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware1
Science: Mathematics1
The category Technology: Engineering (General). Civil engineering (General) 3 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A New Hybrid Ensemble Deep Learning Model for Train Axle Temperature Short Term Forecasting and was published in 2021. The most recent citation comes from a 2024 study titled Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach. This article reached its peak citation in 2022, with 4 citations. It has been cited in 9 different journals, 22% of which are open access. Among related journals, the Journal of Data Analysis and Information Processing cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year