Game IT
An open source, multi-level and highly complex agent-based model simulating real life traffic flow has been developed using the Gama Platform. It models agent behaviours in different vehicle type travelling through a city. The main goal of this model is to show how the future mobility mode can change the local feel of the city. The model and the simulation output helps to see the impact of different mobility modes on traffic flow and congestion. During this workshop may tasks have been addressed, such as data collection and analysis, urban design refinement and land use definition, mobility modes and pathways, commuting patterns depending on agent profiles. It has been designed in close collaboration between the researchers of the Media Lab and a French research team using the GAMA platform.
Define a generic way to describe the data required to construct an agent-based model, including GIS description of the proposed urban plan.
Define the level of detail required for the agent–based simulation: uses of each building (types of residential, types of office, 3rd places schools, cultural facilities, types of parks, etc).
Define the types of mobility modes for the project, including the vehicle characteristics of both existing and new modes (# passengers, speed, autonomous vs. conventional, etc.). Define assumptions about the mobility use patterns of people (agents) that will be used to establish the origin/destination pairs for agent behaviors.
Make assumptions about commuting behaviors: specifically, how many people would live and work in the district and those who commute in each day from outside of the district. Assumptions will also be made about behaviors with respect to people who enter and leave the district for other no-work activities (recreation, shopping, etc.)
Define the profiles of people, numbers, and origin and destination points as determined by places of living, work, and other activities - including people who live in the district and those who commute. Profiles of people living in each type of residential building, working in each type of commercial building, and attracted to 3rd places (students, young professionals, families, mid- career workers, senior executives, etc.). This information will be used to establish the behaviors of each agent type: where they live, where they work, and the "3rd places" that they visit.