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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

Traffic flow prediction; time series; LSTM; XGBoost; deep learning

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.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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