Exam Hall Ticket Management System

Simulation of Ticket Hall Queuing behavior in Transit Station Based on Cellular Automate Model

Cellular Automata (CA) model is extensively applied to pedestrian evacuation simulation in emergency, but little research is about pedestrian behavior of orderly activities in normal situation. Based on queue theory, CA, finite state machine model and visibility graph, queuing behavior and path search algorithm are modeled. Considering the direction of queue activities, a pedestrian locomotion behavior model based on CA is presented. The simulation model is implemented with object-oriented program language(C#). The result shows that the model can simulate the pedestrian behavior in normal situation.


Exam Hall Ticket Management System

Recently, the rapid development of rail transit in china leads client to pay attention to the planning and design of transit station [1]. The research on pedestrian traffic in transit station is more and more important. As many planning problems focus on processes that evolve over time in a complex environment, it is often difficult to evaluate the long term implications of a planning decision. Thus, macroscopic methods are not suitable for planning decision. Microscopic simulations have long been used as a method for evaluating proposed future scenarios in planning. In the paper, Cellular Automata (CA) model is applied in simulating the pedestrian movement behaviors in Ticket Hall area of transit station.

The space in the rail transit station is relatively close, and pedestrian behaviors in the station are little interfered. The activities in the station mainly include queuing for ticket, queuing for entrance or exit, walking on level passage or stair, and waiting for train or getting down from train. The space of transit station is usually divided into two types: toll area and Ticket Hall area. The main behaviors in the Ticket Hall area are, arriving, freely walking, queuing and leaving (see fig.1). Queuing behavior is the most complex. Click Here

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