Probabilistic model for remain passenger queues at subway station platform

Abstract

The remain passenger problem at subway station platform was defined initially, and the period variation of remain passenger queues at platform was investigated through arriving and boarding analyses. Taking remain passenger queues at platform as dynamic stochastic process, a new probabilistic queuing method was developed based on probabilistic theory and discrete time Markov chain theory. This model can calculate remain passenger queues while considering different directions. Considering the stable or variable train arriving period and different platform crossing types, a series of model deformation research was carried out. The probabilistic approach allows to capture the cyclic behavior of queues, measures the uncertainty of a queue state prediction by computing the evolution of its probability in time, and gives any temporal distribution of the arrivals. Compared with the actual data, the deviation of experimental results is less than 20%, which shows the efficiency of probabilistic approach clearly.