Open Access
ARTICLE
Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model
Xijun Zhang*, Qirui Zhang
College of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
* Corresponding Author: Xijun Zhang. Email:
Computer Modeling in Engineering & Sciences 2020, 125(1), 95-109. https://doi.org/10.32604/cmes.2020.011013
Received 14 April 2020; Accepted 02 July 2020; Issue published 18 September 2020
Abstract
According to the time series characteristics of the trajectory history
data, we predicted and analyzed the traffic flow. This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to
analyze and solve the problems of periodicity, stationary and abnormality of time
series. It can improve the traffic flow prediction effect, achieve efficient traffic
guidance and traffic control. The model combined the characteristics of LSTM
(Long Short-Term Memory) network and XGBoost (Extreme Gradient Boosting)
algorithms. First, we used the LSTM model that increases dropout layer to train
the data set after preprocessing. Second, we replaced the full connection layer
with the XGBoost model. Finally, we depended on the model training to strengthen
the data association, avoided the overfitting phenomenon of the fully connected
layer, and enhanced the generalization ability of the prediction model. We used
the Kears based on TensorFlow to build the LSTM-XGBoost model. Using speed
data samples of multiple road sections in Shenzhen to complete the model verification, we achieved the comparison of the prediction effects of the model. The results
show that the combined prediction model used in this paper can not only improve
the accuracy of prediction, but also improve the practicability, real-time and scalability of the model.
Keywords
Cite This Article
Zhang, X., Zhang, Q. (2020). Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model.
CMES-Computer Modeling in Engineering & Sciences, 125(1), 95–109. https://doi.org/10.32604/cmes.2020.011013
Citations