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Investigating congestion in train rapid transit systems (RTS) in today'surban cities is a challenge compounded by limited data availability anddifficulties in model validation. Here, we integrate information from travelsmart card data, a mathematical model of route choice, and a full-scaleagent-based model of the Singapore RTS to provide a more comprehensiveunderstanding of the congestion dynamics than can be obtained throughanalytical modelling alone. Our model is empirically validated, and allows forclose inspection of the dynamics including station crowdedness, average travelduration, and frequency of missed trains---all highly pertinent factors inservice quality. Using current data, the crowdedness in all 121 stationsappears to be distributed log-normally. In our preliminary scenarios, weinvestigate the effect of population growth on service quality. We find thatthe current population (2 million) lies below a critical point; and increasingit beyond a factor of $\sim10\%$ leads to an exponential deterioration inservice quality. We also predict that incentivizing commuters to avoid the mostcongested hours can bring modest improvements to the service quality providedthe population remains under the critical point. Finally, our model can be usedto generate simulated data for analytical modelling when such data are notempirically available, as is often the case.

Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data