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Section: New Results

Scheduling activities under spatial and temporal constraints

Participants : Fabrice Lamarche, Carl-Johan Jorgensen.

This work focusses on generating statistically consistent behaviors that can be used to pilot crowd simulation models over long periods of time, up to multiple days [1] . In real crowds, people’s behaviors 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 cdomain of activity scheduling and path planning. First, we proposed 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 showed that this model produces schedules statistically coherent with the ones produced by humans in the same situations. Second, we proposed 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. Finally, those models have been included in a simulation platform that is able to simulate several thousand of pedestrians performing their daily activities in real-time. In order to deal with unexpected events, a process enabling adaptations of the pedestrian behavior have been designed. Those adaptations range from path modification to schedule adaptation according to the observed situation.

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’ behaviors. It can be used to easily populate virtual cities in which observable crowd phenomena emerge from individual activities.