NYU Students Learn to Predict the Effects of Pandemics on Transportation Networks Using Agent-Based Simulation
In a recent class, NYU Tandon graduate students learned how to create an agent-based simulation (ABS) model to predict the effects of pandemics using software called NetLogo, a java-based modeling and simulation tool. ABS models are micro-scale models that simulate the continuous interactions of multiple agents to re-create, analyze and predict the behavior of complex phenomena in the real world, such the effects of a pandemic on mobility.
Transportation Planning and Engineering, and Transportation Management students enrolled in Data-Driven Mobility Modeling & Simulation were introduced to A Susceptible-Infected-Recovered (SIR) disease spread model adopted from Paul E. Smaldino. The model reflects the importance of social distancing during the COVID-19 outbreak. By controlling the size of the step each agent takes with each movement, students were able to manipulate the “social-distance” of the agents in the model and were able to simulate the spread of the disease when “flattening the curve” compared to its spread without intervention. The SIR model parameters are coded into the simulation as dynamic variables and can be controlled by the user. The developed model can be accessed online.