Combination predicting model of traffic congestion index in weekdays based on LightGBM-GRU

• Published in: Scientific reports Vol. 12; no. 1; pp. 2912 - 13
• Main Authors: Cheng, Wei, Li, Jiang-lin, Xiao, Hai-Cheng, Ji, Li-na
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.02.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/986; 639/705; 639/705/1046; Accuracy; Algorithms; Datasets; Deep learning; Humanities and Social Sciences; Machine learning; multidisciplinary; Neural networks; Periodicity; Prediction models; Science; Science (multidisciplinary); Traffic congestion; Traffic control
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-06975-1

Tree-based and deep learning methods can automatically generate useful features. Not only can it enhance the original feature representation, but it can also learn to generate new features. This paper develops a strategy based on Light Gradient Boosting Machine (LightGBM or LGB) and Gated Recurrent Unit (GRU) to generate features to improve the expression ability of limited features. Moreover, a SARIMA-GRU prediction model considering the weekly periodicity is introduced. First, LightGBM is used to learn features and enhance the original features representation; secondly, GRU neural network is used to generate features; finally, the result ensemble is used as the input for prediction. Moreover, the SARIMA-GRU model is constructed for predicting. The GRU prediction consequences are revised by the SARIMA model that a better prediction can be obtained. The experiment was carried out with the data collected by Ride-hailing in Chengdu, and four predicted indicators and two performance indexes are utilized to evaluate the model. The results validate that the model proposed has significant improvements in the accuracy and performance of each component.