Short-Term Public Transportation Passenger Flow Forecasting Method Based on Multi-source Data and Shepard Interpolating Prediction Method

Abstract

The accurate passenger flow prediction is the base of bus scheduling and bus dispatching. Many factors, including internal factors and external factors, have great impact on the fluctuation of passenger flow. In the modern informationized bus system, many influencing factors became available by multi-source data. Current passenger flow prediction methods are mainly based on statistical predicting methods and machine learning methods. The implication of interpolating prediction method on passenger flow prediction is preliminary. Interpolating prediction method makes use of historical data; the prediction result is generally accurate, and the method is robust. Interpolating prediction method shows good performance and has mature application in other research areas. This paper makes use of historical passenger data and multi-source data; apply Shepard model to predict public transportation passenger flow. The result shows that Shepard prediction model has better performance than that of neural network (NN) model and support vector machine (SVM) model. The mean absolute percentage error (MAPE) has increased 7.5 and 3.43%; the MSP has increased 16 and 10.51% compared with NN and SVM and has lower dependency of parameters.