Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19

Abstract

At present, machine learning has been successfully applied in many fields and has achieved amazing results. Meanwhile, over the past few years, the pandemic has transformed the transportation industry. The two hot issues prompt us to rethink the traditional problem of passenger flow forecasting. As a special structure embedded in the machine learning model, the attention mechanism is used to automatically learn and calculate the contribution degree of input data to output data. Therefore, this paper uses the attention mechanism to find the best model to predict OD passenger flow under COVID-19. Holiday characteristics, minimum temperature, COVID-19 factors, and past origin-destination (OD) passenger flow were used as input characteristics. In the first stage, the attention mechanism was used to capture the advantages of the trained random forest, extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), and Adaboost models, and then the MLP was trained. Afterward, the weight distribution of the two models is carried out by using the historical passenger flow. The multi-model attention+ MLP model was used to evaluate the OD passenger flow prediction of Dalian Metro Line 1 under COVID-19. All the possible choices in this process were taken as a comparison experiment. The results show that only the fusion model combining the attention mechanism of random forest and XGBoost with MLP has the highest prediction accuracy.