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Journal of Sensors
Volume 2017, Article ID 7074143, 15 pages
https://doi.org/10.1155/2017/7074143
Research Article

Ensemble Learning for Short-Term Traffic Prediction Based on Gradient Boosting Machine

1Institute of Transportation Engineering, Department of Civil Engineering, Tsinghua University, Beijing 100084, China
2School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Correspondence should be addressed to Senyan Yang; moc.621@gnaynaynes

Received 19 December 2016; Revised 5 March 2017; Accepted 19 March 2017; Published 4 May 2017

Academic Editor: Fanli Meng

Copyright © 2017 Senyan Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Short-term traffic prediction is vital for intelligent traffic systems and influenced by neighboring traffic condition. Gradient boosting decision trees (GBDT), an ensemble learning method, is proposed to make short-term traffic prediction based on the traffic volume data collected by loop detectors on the freeway. Each new simple decision tree is sequentially added and trained with the error of the previous whole ensemble model at each iteration. The relative importance of variables can be quantified in the training process of GBDT, indicating the interaction between input variables and response. The influence of neighboring traffic condition on prediction performance is identified through combining the traffic volume data collected by different upstream and downstream detectors as the input, which can also improve prediction performance. The relative importance of input variables for 15 GBDT models is different, and the impact of upstream traffic condition is not balanced with that of downstream. The prediction accuracy of GBDT is generally higher than SVM and BPNN for different steps ahead, and the accuracy of multi-step-ahead models is lower than 1-step-ahead models. For 1-step-ahead models, the prediction errors of GBDT are smaller than SVM and BPNN for both peak and nonpeak hours.