The Use of Monte Carlo Simulation to Evaluate the Passenger Average Travelling Time Under Up-peak Traffic Conditions

Monte Carlo simulation is a powerful tool used in calculating the value of a variable that is dependent on a number of random input variables. For this reason, it can be successfully used when calculating the round trip time of an elevator, where some of the inputs are random and follow pre-set probability distribution functions. The most obvious random inputs are the number of passengers boarding the car in one around trip, their origins (in the case of multiple entrances) and their destinations. Monte Carlo simulation has been used to evaluate the elevator around trip time under up-peak traffic conditions. Its main advantage over analytical formula based methods is that it can deal with all special conditions in a building without the need for evaluating new special formulae. A combination of all of the following special conditions can be dealt with: Unequal floor population, unequal floor heights, multiple entrances and top speed not attained in one floor jump. Moreover, this can be done without loss of accuracy, by setting the number of runs to the appropriate value. This paper extends the previous work on Monte Carlo simulation in relation to two aspects: the passenger arrival process model and the passenger average travelling time. The software is developed using MATLAB. The results for the average travelling time are compared to analytical formulae (such as that by So. et al., 2002). The results showing the effect of the Poisson arrival process on the value of the elevator round trip time are also analysed. The advantage of the method over analytical methods is again demonstrated by showing how it can deal with the combination of all the special conditions without the loss of accuracy (five conditions if the passenger arrival model is added as Poisson). The issues of convergence, accuracy and running time are discussed in relation to the practicality of the method.