Short-term Traffic Flow Forecasting of Road Based on XGBoost Model
Based on XGBoost model,the urban road traffic data collected by radar equipment in Yangpu District,Shanghai was used to predict the road traffic.Considering the complexity and randomness of urban road traffic flow,31 characteristic variables including overall characteristics,time-related char-acteristics and space-related characteristics were selected,and the main parameters of the model were adjusted by grid search.The results show that the RMSE accuracy of XGBoost model is better than the other five comparative models in different time granularity,and it also has advantages in efficien-cy.When the time granularity is 5 minutes,the RMSE value is 14.22 and the MAPE value is 0.153,which takes 23.84s s.In addition,XGBoost is highly interpretable.Through the combination predic-tion of different characteristic variables and the analysis of the importance of characteristic variables,it is found that the 1st,2nd and 3rd order lag flows and their differences can obviously improve the prediction accuracy of the model.With the increase of time granularity,the periodicity of flow increa-ses and the randomness decreases.
traffic volumeshort-term traffic predictionmachine learningextreme gradient boosting trees
万方数据
维普
