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A Simulation-based Method for Predicting the Time-varying Passenger Demand at Metro Rail Transit Line 3 Using Monte Carlo Simulation

Simulation methods consider the inherent randomness and uncertainty in modeling a system. The purpose of the current study was to provide a practical approach to estimate and predict the monthly inbound passenger flow of the Manila Metro Rail Transit Line 3 (MRT-3) by utilizing the Monte Carlo Simulation (MCS) method. MCS was used to generate random numbers from this distribution and was able to produce a reasonably forecasted model. By using the monthly passenger data, it was found that Logistic distribution had the best fit to estimate the given data. The results derived in this study could provide valuable insights, particularly on capacity planning, which can be used to improve the efficiency and sustainability of the existing rail system of MRT-3.