A single train breakdown during rush hour can send the smoothly-running flow of commuters into a tailspin. Frantic crowds hurry for buses and taxis, only to be frustratingly met by snaking queues.
Densely-populated cities like Singapore need contingency plans to ensure their high volumes of commuters can seamlessly transition between transport modes when major systems, such as rail lines, are disrupted. These plans can include regular and accurate communication with commuters; effective service rerouting; and rapid deployment of alternative transport modes like bridging buses.
“Transport simulation software can be used to evaluate different scenarios, such as the impact of mitigation measures during on commuter travel time during a train line disruption, or estimated levels of congestion at the train stations," said Vasundhara Jayaraman, a Lead Research Engineer at A*STAR’s Institute of High Performance Computing (IHPC). “Such simulations not only help city planners and governments understand how transportation changes impact traffic, commuter times and overall mobility, but can also be used to test policies like transportation subsidies.”
Vasundhara and Rakhi Manohar Mepparambath, a Senior Scientist at IHPC’s Systems Science Department, were part of a team that developed a simulation platform calibrated with real-world data from Singapore. Through a co-simulation approach, SUMMIT (Singapore Urban Multi-Modal Integrated Transport Simulator) integrates train, bus and taxi simulators to facilitate seamless commuter transitions between transport modes. The platform also uses a message-passing framework codenamed ‘Fabric’ to synchronise simulations and manage transitions.
Vasundhara explained that SUMMIT's co-simulation approach with Fabric integrates different independently developed and calibrated models to allow efficient, flexible and scalable commuter behaviour simulations. The platform does not rely heavily on primary surveys, as it uses existing data from sources such as GPS and farecards.
Rakhi shared that most other existing simulation projects that also model train disruption scenarios did not calibrate the simulated commuter behaviours using real-world data from actual disruptions. “SUMMIT is calibrated using real-world datasets such as those from past train-line disruption events in Singapore. It can turnaround a full-day simulation of bus, train and taxi systems at a city-wide scale relatively quickly, completing multiple simulation evaluations within hours,” added Rakhi.
By running scenarios using SUMMIT, the team found that while bridging bus services can generally reduce station crowd sizes during train disruptions, overall commuter travel times could still increase due to over-demand at bus stops. Simulations also showed that early dissemination of disruption information can reduce negative impacts on commuters significantly, as commuters could plan earlier for travel journeys to avoid heavily congested areas.
By improving contingency planning, the SUMMIT platform—a project supported by the National Research Foundation and the Land Transport Authority of Singapore—can enhance commuter satisfaction and the reliability of public transport systems, the team concluded.
The A*STAR-affiliated researchers contributing to this research are from the Institute of High Performance Computing (IHPC).
