A Hybrid Ensemble Model for Urban Lane-Level Traffic Flow Prediction

In recent years, automatic driving and Internet of Vehicles technology have made great progress. Road oriented the traditional traffic flow prediction methods can no longer meet the needs of automatic driving, lane-level map navigation. This paper takes urban lanes as the research object, and proposes a traffic flow prediction model for urban lanes. First, a boosting based Catboost model is introduced to construct a series of spatiotemporal features and perform feature selection to reduce the model bias. Second, the model variance is reduced by using bagging-based random forest algorithm. Third, a long short-term memory network (LSTM) is used to extract the temporal trend of traffic flow in the current lane. The prediction results of these three models are finally integrated by the method of stacking ensemble learning. The proposed method is evaluated with the dataset collected from real intersections in Wuxi city. The experimental results show that our model has higher prediction precision than other methods.