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City-wide Public Transport Simulation (PTSim) Model
Ensuring high-quality public transportation systems is a key solution to solve road congestion issues in metropolitan cities. However, this problem is highly complex and challenging to planners, regulators and operators alike. Existing transport modelling softwares are typically vehicle/traffic-centric and provide inadequate details on public transport commuter service quality. While the advent of individual mobility datasets such as farecard data could potentially solve this issue, using such large datasets is also highly challenging for non-technical users due to difficulties in ensuring scalability and modelling accuracy.
IHPC’s City-wide Public Transport Simulation (PTSim) model of òòò½Íøis a computationally efficient and scalable solution to city-wide models. It provides adequate fine-grained details on individual commuter’s travelling times and other associated transport performance quality metrics. The outputs include the overall crowdedness levels of all buses and trains in the entire city at any given time on a typical weekday. PTSim utilises a unique mix of efficient big data analytics, complex network modelling, behavioural analysis and high performance agent-based simulation.
Features
- Analyse and visualise travel patterns across the city
- Simulate a dynamic model of city-wide public transport system
- Perform and visualise impact of a simplified rail disruption scenario
- Validate simulated travelling times against actual historical commuter’s travel data
The Science Behind
PTSim combines the use of agent-based and discrete-event simulation.
An agent-based simulation is a class of computational models that simulate entities (also known as agents) on an individual basis compared to modelling them on a collective basis. For instance, instead of representing the flow of buses on the road using a numerical variable (i.e., 5 vehicles per minute), each vehicle is modelled as an entity with specified state variables. Despite being computationally intensive, the agent-based model provides details that could allow a broader range of applications and simulation features to be modelled, including emergent effects caused by interactions between individual agents and their environment. Agent-based simulations also can model complex effects, such as the impact of anomalous events on agent behaviour and travel choices (for example, crowdedness effects during train disruption events).
A discrete-event simulation (DES) is a high performance computing technology used to model events occurring in simulated time. Instead of modelling time in fixed increments (for example, second by second), a DES models the system as a sequence of events happening at different time stamps. A DES model can complete a simulation run much faster than a corresponding fixed increment simulation by moving time forward ahead towards the next notable event and skipping through periods of time without significant events, depending on the frequency of events modelled.
Industry Applications
Various end-user applications have already been implemented using this base platform. This includes a system-wide evaluation of changes in transport demand, prediction of regularity performance of public bus service operations as well as modelling of disruption scenarios and evaluation of contingency plans.
For more info or collaboration opportunities, please write to enquiry@ihpc.a-star.edu.sg.
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