Abstract We set up a mechanistic agent-based model of a rapid mass transit system. Using empirical data from Singapore’s unidentifiable smart fare card, we validate our model by reconstructing actual travel demand and duration of travel statistics. We subsequently use this model to investigate two phenomena that are known to significantly affect the dynamics within the RTS: (1) overloading in trains and (2) overcrowding in the RTS platform. We demonstrate that by varying the loading capacity of trains, a tipping point emerges at which an exponential increase in the duration of travel time delays is observed. We also probe the impact on the rail system dynamics of three types of passenger growth distribution across stations: (i) Dirac delta, (ii) uniform and (iii) geometric, which is reminiscent of the effect of land use on transport. Under the assumption of a fixed loading capacity, we demonstrate the dependence of a given origin–destination (OD) pair on the flow volume of commuters in station platforms.