Accurate traffic flow prediction under abnormal conditions, such as accidents, adverse weather, work zones, and holidays, is significant for proactive traffic control. Here, the authors focus on a special challenge of how to develop robust responsive algorithms and prediction models for short‐term traffic forecasting in different traffic conditions. To this end, this study presents an ensemble learning algorithm for the short‐term traffic flow prediction via the integration of gradient boosting regression trees (GBRT) and the least absolute shrinkage and selection operator (Lasso). Four different model structures whether considering the feature selection are proposed and tested for multi‐step‐ahead prediction under both normal and abnormal conditions. The results indicate that the proposed multi‐model ensemble models are superior to the benchmark algorithms, i.e., support vector regression, and random forests, the GBRT model outperforms the Lasso model under normal traffic conditions, and the Lasso model has a better prediction accuracy under abnormal traffic conditions. In addition, the Lasso model with the feature selection is superior to the full feature model under either normal or abnormal conditions, while the GBRT model is not always better under normal conditions. The proposed integration framework is general and flexible to assemble various traffic prediction algorithms.
Multi‐model ensemble for short‐term traffic flow prediction under normal and abnormal conditions
IET Intelligent Transport Systems ; 13 , 2 ; 260-268
2019-02-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
data analysis , ensemble learning algorithm , training set , traffic flow data , urban expressway , traffic conditions , random forests , least absolute shrinkage‐and‐selection operator model , multimodel ensemble models , robust responsive algorithms , road traffic , traffic engineering computing , abnormal conditions , short‐term traffic forecasting , trees (mathematics) , GBRT model , regression analysis , normal conditions , feature selection , short‐term traffic flow prediction , support vector regression , learning (artificial intelligence) , support vector machines , Lasso model , remote traffic microwave sensors , gradient boosting regression trees integration model , multistep‐ahead prediction , test set
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