1 research outputs found
Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data
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 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 analytical modelling when such data are not
empirically available, as is often the case.Comment: 10 pages, 5 figures, submitted to International Conference on
Computational Science 201