Data-Driven Agent-Based Model of Intra-Urban Activities

We propose an agent-based model (ABM) to simulate city-scale intra-urban activities and movements. We calibrate the ABM for New York City, using NYC Open Data trip diaries and taxi journeys. Model validation demonstrates that the ABM is able to accurately predict activity demand across the city. Further, when a new hospital wing is opened in Queens, a central district of New York City, the ABM is shown to accurately predict increased shopping demand on Staten Island, an isolated area located at the edge of the city. This demonstrates the value of applying ABM to simulate intra-urban movements and activities, offering dynamic scenario testing that is not available in many other forms of modelling.