Research on propagation of passenger flow in urban rail transit network for large-scale events

Large-scale events in cities will attract a large number of audiences and bring great pressure on the nearby traffic operation and management. However, little attention has been given to the evaluation and propagation in urban rail transit network. The objective of this work is to propose an effective dynamic traffic assignment method for large-scale events and capture the temporal-spatial nature of passenger flow in the complex metro network. In the method framework, a stochastic utility function including in-vehicle time, extra waiting time and transfer penalties is developed to describe passenger perception and the corresponding estimation method of parameters is given. What's more, an improved multinomial logit model is proposed to take correlation between alternative routes into consideration. Finally, an empirical experiment based on Beijing Metro is conducted to illustrate the propagation of passenger flow after a large-scale event ends and validate the proposed method. The results indicate that an acceptable accuracy can be obtained for analyzing the dynamic influence of large-scale events in urban rail transit network.