Hybrid model for predicting anomalous large passenger flow in urban metros

Machine learning models have been widely adopted for passenger flow prediction in urban metros; however, the authors find machine learning models may underperform under anomalous large passenger flow conditions. In this study, they develop a prediction framework that combines the advantage of complex network models in capturing the collective behaviour of passengers and the advantage of online learning algorithms in characterising rapid changes in real‐time data. The proposed method considerably improves the accuracy of passenger flow prediction under anomalous conditions. This study can also serve as an exploration of interdisciplinary methods for transportation research.