Short-term passenger flow forecast for urban rail transit based on multi-source data

Abstract Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic model. The built using intelligent data provided large-scale warning system, such as accurate data, collected Internet of things sensor networks. proposed this paper can adapt complexity, nonlinearity, periodicity transit. Test results Beijing dataset show that SARI-MA–SVM improve accuracy reduce errors prediction. obtained pre-diction fits well with measured data. Therefore, SARIMA–SVM fully charac-terize variations suitable for