Scheduling activities under spatial and temporal constraints to populate virtual urban environments

Abstract

Crowd simulation models usually aim at producing visually credible crowds with the intent of giving life to virtual environments. Our work focusses on generating statistically consistent behaviours that can be used to pilot crowd simulation models over long periods of time, up to multiple days. In real crowds, people's behaviours mainly depend on the activities they intend to perform. The way this activity is scheduled rely on the close interaction between the environment, space and time constraints associated with the activity and personal characteristics of individuals. Compared to the state of the art, our model better handle this interaction. Our main contributions lie in the domain of activity scheduling and path planning. First, we propose an individual activity scheduling process and its extension to cooperative activity scheduling. Based on descriptions of the environment, of intended activities and of agents' characteristics, these processes generate a task schedule for each agent. Locations where the tasks should be performed are selected and a relaxed agenda is produced. This task schedule is compatible with spatial and temporal constraints associated with the environment and with the intended activity of the agent and of other cooperating agents. It also takes into account the agents personal characteristics, inducing diversity in produced schedules. We show that our model produces schedules statistically coherent with the ones produced by humans in the same situations. Second, we propose a hierarchical path-planning process. It relies on an automatic environment analysis process that produces a semantically coherent hierarchical representation of virtual cities. The hierarchical nature of this representation is used to model different levels of decision making related to path planning. A coarse path is first computed, then refined during navigation when relevant information is available. It enable the agent to seamlessly adapt its path to unexpected events. The proposed model handles long term rational decisions driving the navigation of agents in virtual cities. It considers the strong relationship between time, space and activity to produce more credible agents' behaviours. It can be used to easily populate virtual cities in which observable crowd phenomena emerge from individual activities.