The choreography of pedestrian movement is important to many domains of interest, particularly in the geographical sciences. Agent-based models have become a popular tool for simulating movement, allowing experimentation with scenarios in computer models that might not be amenable to real-world investigation. The fidelity of agent-based movement models is naturally most acute when the models driving their synthetic characters reproduce the geography of their behaviors appropriately: by placing people in the right places, at the right times, doing the right things, in the right contexts. However, most simulation environments for moving agent pedestrians rely on simple, abstract physical heuristics to drive synthetic characters and they focus on generating plausible coarse-grained movement patterns, which might not always map to real-world pedestrian behavior. Moreover, existing approaches often produce serious mechanical artifacts in simulation. I contend that agent-based models of pedestrian movement can benefit more fully from a comprehensive infusion of realistic movement behavior and I present the case for, and proven usefulness of, a geographic engine for driving synthetic actors in simulation. While many existing approaches use particle physics, my approach is sourced in theory and observation, modeling lower-, medium- and higher-level behavioral geographies for perceiving and sorting objects, route-planning and way-finding, orientation and locomotion, physical steering, mediating interactions, and determining the space and time for scheduling and realizing activities, with the results that the scheme that I present can automatically generate realistic-looking and realistic-behaving synthetic pedestrians for experimentation.

See our projects on immersive modeling also.

See an earlier lecture on the topic here.

The model treats high-level, medium-level, and low-level behavioral geography. Above, the determination of a space-time path between a trip source and sink is displayed, as an example of high-level behavioral geography. A variety of path-planning algorithms can be used.

Path-planning can be flexibly applied in a diversity of environments: the agents can be dropped into any context and they will resolve an efficient path (using any guided heuristic you supply them).

Medium-level behavioral geography: above, wayfinding between fleetingly-resolved landmarks (lamp-post markers on street corners, in this case) is shown.

At a lower-level of behavioral geography, steering and locomotion are used by agents to articulate their movement through space and time, while seeking-out or avoiding collisions with fixed and mobile objects.

The model works seamlessly with Geographic Information Systems (it is built on a Geographic Automata infrastructure)

The model works as a pipeline of integrated, co-functioning, modules. These can be swapped interchangeably without necessarily affecting the overall flow of information through the pipeline.

The idea is that we may build a sophisticated and realistic urban infrastructural environment, that provides substrate for agents' actions, interactions, and activities. We can "drop" the agents into this setting--or any setting--and they should be able to use their endowed behavior to plan activities, navigate, way-find, move, and interact naturally. This environment can be changed, malleably, as an experiment suits and the agents should adjust their behavior accordingly.

Big data movement analytics

Land indicators of climate

High-performance computing and networking for geosimulation

Earthquake models

Ice-sheet modeling

Dynamic physics for built infrastructure

Moving agents through space and time

Validating agent-based models

Machine-learning behavioral geography

Accelerating agent-based models

Megacity futures

Immersive modeling

Space-time GIS and analysis

A toolkit for measuring sprawl

Modeling time, space, and behavior