Traffic micro-simulation models have become widely accepted tools in analyzing and identifying solutions for vehiclebased
infrastructures. Similarly, pedestrian micro-simulation models despite their relative infancy can also be utilized in
a similar manner for pedestrian-dominated facilities. Pedestrian micro-simulation models offer an innovative approach to
evaluating scenarios without risk of injury to subject pedestrians. Pedestrian micro-simulation models also have the advantage
of graphic visualization for presentation of results to the general public. Analogous to conventional models, pedestrian
micro-simulation models are also tested to make certain necessary pedestrian activities (waiting, queuing, walking or processing)
are modeled accurately. One of the more important components of public transport terminals and large-crowd facilities
(e.g. stadia, cinemas, etc.) are waiting line systems (queue and servers) wherein of particular importance is the adequacy
of waiting areas. There are two prevailing methods for estimating the maximum queue length which eventually translates to
the waiting area: using well-established queuing theory or micro-simulation. Analytical modeling that requires equations to
estimate the performance of the system experiences difficulty in mimicking all real-world situations as numerous variables
exist in some cases in which appropriate equations are unknown or the equation is too complicated. On the other hand, a
pedestrian micro-simulation model can be utilized without the boundary of restricted assumptions (Sokolowski and Banks,
2009).
This paper investigates the implementation of a single queue with a multiple servers system of a cinema ticketing office
using the pedestrian micro-simulation software Vissim. Vissim pedestrian module employs the ‘Social Force Model’ published
by Helbing and Molnar (1995) which was designed to represent the stochastic behavior of pedestrian movements. The system
tested is characterized by a single queue serviced by a number of ticket booths. When customers arrive at the ticketing
area, some may need to wait if there is no booth available to service them resulting in the formation of a queue. When service
rates are less than the arrival rates, no queue will form. Regardless, it is essential that all four characteristics of queues (pedestrian
arrival, service times, number of serving channels, and queuing discipline) are correctly modeled.
This paper is organized as follows. The following section briefly discusses past work on queuing studies and pedestrian
micro-simulation. The section following that details field data collection, the methodology and micro-simulation set-up.
Analysis of the modeling results are presented next followed by a case study application of the method. The paper ends with
the presentation of relevant findings.