Short-term Prediction of Suzhou Rail Transit Passenger Flow Based on Combination Model

With the increasing economic development of China, the country encourages to develop public transport strongly, and urban rail transit has become a choice for more and more cities. But for rail transit operations, passenger flow prediction is becoming more and more important and has become a key issue in transportation planning. However, the effect of a single model on predicting short-term passenger flow is not ideal. Therefore, this study proposes a combined model based on GA-BP neural network and forecasts the passenger flow of Suzhou Urban Rail Transit Line 1 according to weather, holidays, and other factors. Meanwhile, the study compares with the ARIMA and BP neural network models. The results show that the accuracy of GA-BP model improved by 6.06% and 8.69% respectively which compared with the former, and the results have improved the accuracy of passenger flow prediction effectively. It is proved that the combined model has certain practical value.