Short-Term Traffic Flow Prediction Based on Combination Model of Xgboost-Lightgbm

摘要

In modern intelligent transportation system, traffic control and traffic congestion management are main components of it. Real-time and accurate short-term traffic flow prediction is the precondition and key to realizing traffic control and traffic congestion management. In order to improve the accuracy of short-term traffic flow prediction, a combined model prediction method is proposed in this paper which is based on Xgboost and LightGBM algorithms. First, the short-term traffic flow data is preprocessed and features are sampled. Then, according to different characteristics, different prediction models are constructed by using Xgboost and LightGBM algorithms. Finally, these models are merged to generate the final model. Appling this model for prediction, the average travel time of the road can be obtained to predict the traffic flow and reflect road congestion. Experimental results show that the combined model has higher prediction accuracy than a single model, and it is an effective short-term traffic flow prediction method.