# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 6 Conclusions  
Traffic flow forecasting has been a significant and hot research topic during the past decades because of its key role in proactive traffic management and control. In this paper, a short-term traffic flow forecasting approach based on multi-regime modeling and ensemble learning is presented. The approach consists of two procedures. In the first procedure, multiple regimes of traffic flow were properly identified using a probabilistic modeling method, and further used to construct the representative features for the forecasting modeling. In the second procedure, the constructed features  
Figure 7: Comparisons between the observed and forecasted values of the three traffic flow measures.  
were utilized to establish the training and test data sets that are employed to train and evaluate the forecasting model. To improve the generalization performance of the model, an ensemble learning strategy from the machine learning domain was introduced. To evaluate the proposed approach, 5-min traffic flow data collected from four dual-loop detectors on I-80 freeway segments were used. The experimental results show that the identified regimes are able to explain the different phases of traffic flow well, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four comparative models in terms of RMSE and MAPE on three traffic flow measures.  
For a forecasting model, its accuracy is closely associated with two aspects, that is, data and algorithm. To ensure the quality of the used data, the representative features need to be properly determined. In the study, we developed a multi-regime modeling strategy to enable the model to have good input features. On the other hand, an elegant algorithm needs to be designed to make the forecasting model well-fitting with the training data, while possessing a good generalization ability. To achieve this goal, a typical ensemble learning strategy was employed in the study. The above two strategies ensure the good performance of the proposed approach.  
In the future, more traffic flow data collected from different road types will be used to evaluate the proposed approach. As the number of identified regimes could affect the performance of the forecasting model, it is necessary to check the sensitivity of the parameter and to explore how to optimally determine the parameter. In addition, more forecasting models will be implemented and compared. Finally, more multi-regime modeling and ensemble learning strategies will be developed and integrated into the framework of the proposed approach.  
The paper was written by [PERSON] in collaboration with all co-authors. The model was established and tested by [PERSON] and [PERSON]. The data was collected by Q.N. The research and key elements of the models were reviewed by [PERSON] The major revision of this paper was completed by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded by National Natural Science Foundation of China (grant number 71871055).  
The authors declare that there is no conflict of interest.