Short-Term Prediction of Urban Rail Transit Passenger Flow in External Passenger Transport Hub Based on LSTM-LGB-DRS

This paper studies accurate short-term prediction of urban rail transit passenger flow in external passenger transport hub. Based on the conventional features that affect external rail transit passenger flows, we propose an innovative method of statistical features construction to make accurate prediction of frequently fluctuating passenger flow under the framework of machine learning. Firstly, by classifying the statistical features in time-series, we improve the previous long-short-term memory(LSTM) network model and establish a novel long-short-term memory(LSTM) network model that can deal with long-term dependent time-series data so as to accurately fit real-time passenger flow. Secondly, by introducing the lightweight implementation algorithm LightGBM based on the gradient decision boosting tree(GBDT), we build a short-term prediction model, which can reduce operation cost and improve accuracy so that accurate fitting of passenger flow peaks can be achieved. Thirdly, based on K-nearest neighbor algorithm, we select DRS as the dynamic regression device that can find a local optimal fusion method, and establish a LGB-LSTM-DRS local optimal fusion prediction model. Taking the AFC passenger flow data of Chengdudong Station as an example, we predict the short-term passenger flow. Compare with other commonly used models such as LR, RF, and GBDT, it's proved that the LGB-LSTM-DRS model has the smallest error (MAPE and RMSE) and the best prediction performance.