Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data
06 Jun 2014

Submitted by (Bold are A*Star Staff): Nasri Nin Othman,Erika Fille Legara,Vicknest Selvam,Christoper Monterola

Research Institute: IHPC

Title of Paper: Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data

Published in: Procedia Computer Science Volume 29, 2014, Pages 1610 - 1620

Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains—all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of approximately 10% leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for statistical analysis when such data are not empirically available, as is often the case.


Acknowledegment remark in publication: and by the A*STAR Computational Resource Centre through the use of its high performance computing facilities.