Modeling Passenger Behavior in Nonpayment Areas at Rail Transit Stations

The nonpayment area at urban transit stations in China usually becomes extremely crowded during peak hours because many passengers queue to buy tickets and pass through the fare gates. How to evaluate the performance of these activities is a critical issue for the design and management of the nonpayment area. This study used microscopic simulation models to investigate passenger behavior in the nonpayment area. The study developed a queue choice model, a passenger movement model, and a path navigation model. Some new ideas were involved. First, the study introduced the concepts of dynamic queue length and dynamic distance between the current passenger and alternative queues into the queue choice model. Second, a new factor, called direction of goal, was proposed to navigate a passenger through the dynamic end of a queue or other goals. This factor was used to construct the transition probability function of a cellular automata model. Finally, the proposed models were calibrated and verified on the basis of a field survey and sensitivity analysis. The results show that the proposed models can capture passenger behaviors in the nonpayment area and perform well for queue estimation.