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It is generally accepted that passenger arrivals for lift service follow a Poisson arrival process. Moreover, recent research has also shown that the arrivals take place in batches rather than single passenger arrivals. For these reasons, lift traffic simulation software may use the Poisson batch arrival process to generate the time of each batch a...
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Context 1
... t ∆ is the inter-arrival time between two consecutive passengers in s ln is the natural logarithm (base e logarithm) λ the passenger arrival rate in passengers per second Rand() is a random number between 0 and 1 An example of a number of passenger arrivals generated using an exponential probability density function for the inter-arrival time with an average of 5 seconds (1/0.2) is shown in Figure 7. This use of the exponential pdf for generating the passenger is the most practical method for generating passenger arrivals. ...
Citations
... It is also possible to generate random arrival times for passengers (i.e., generating passengers in time) assuming a Poisson passenger arrival process [8]. However, this is beyond the scope of this paper, and would not have any impact on the results. ...
The Monte Carlo simulation method has been successfully applied in elevator traffic engineering in a number of areas such as calculating the value of the round trip time under conventional group control and under destination group control, to find the value of the average travelling time, to evaluate the effect of sectoring on the boost in handling capacity and in finding the round trip time for double decker elevators under incoming traffic conditions. This paper develops a new application of the Monte Carlo simulation method in which the full information on the car loading in a round trip is compiled, represented by the probability density function (PDF). This is based on deriving the origin-destination matrix for passenger journeys and then running the Monte Carlo simulation for a large number of trials and extracting the data needed to compile the PDF. The information contained in the PDF for the car loading can then be used to make an informed decision as to the most suitable car capacity under general traffic conditions.
... It is also possible to generate random arrival times for passengers (i.e., generating passengers in time) assuming a Poisson passenger arrival process [18]. However, this is beyond the scope of this paper. ...
The Monte Carlo Simulation (MCS) method has been successfully applied in lift traffic systems to evaluate a number of different parameters such as the round-trip time and the average travelling time; and under different conditions, such as sectoring control and for multiple lift cars running in the same shaft. Using the Monte Carlo Simulation methods is particularly effective in cases where the number of possible combinations becomes too complicated for analytical equation-based calculation methods to deal with. This paper attempts to extend the applications of the Monte Carlo Simulation method in two areas: Car capacity and drive-motor system sizing. It uses the Monte Carlo simulation method to compile the probability density functions (PDF). In the first area, MCS is used in order to compile the statistical description of the number of passengers in the lift car whenever it departs from a floor. This is concisely presented in the form of a probability density function of the number of passengers in the car when it departs from a floor. Such a probability density function can be used to make judicious decisions regarding the suitable car capacity. The second area involves using the same data in order to compile a load profile of the number of passengers inside the lift car when it stops at a certain floor and when it departs from the that floor, along with the probability of the lift car stopping at that floor. This provides a strong input to simulate the drive-motor system and evaluate the temperature rise in the windings and the power electronic devices. A numerical example is given for a single lift car to illustrate the application of the method.
... 2-The Inter-arrival time k Δt between two successive passengers under λ arrival rate intensity can be calculated using the Poisson model ( [6], [7]) assuming that the inter-arrival time follows an exponential probability distribution. ...
