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Dr. Paul M. Torrens, Center for Urban Science + Progress, New York University

Moving agent-pedestrians through space and time

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Project overview

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.

Moving agents (you will need the Adobe Flash Player plug-in for your browser to view this movie)
Eye candy
(Print-quality versions of these graphics are available upon request)
crowd evacuation
The simulation will run agnostically relative to the spaces provided as substrate. Above, a generic half-space is used to explore lane formation as a solution to natural congestion in crowds. Below, more complicated urban scenes are introduced.


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

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.


locomotion and steering

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.

space-time path visualization

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

model schematic

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.

3d agent GIS urban model

3D urban model

3d urban model agent-based model pedestrian simulation

Torrens, P.M. (2007-2012) “CAREER: Exploring the dynamics of individual pedestrian and crowd behavior in dense urban settings: a computational approach”. National Science Foundation (Faculty Early Career Development (CAREER); Geography & Regional Science/ Methodology, Measurement, and Statistics)
nsf Torrens, P.M; Ghanem, Roger; Kevrekidis, Yannis (2010-2011). "Accelerating innovation in agent-based simulations: Application to complex socio-behavioral phenomena". National Science Foundation (Division of Civil and Mechanical Systems)
 Torrens, P.M. (2006) “Urban simulation at the micro-level”. Autodesk, Inc.
Related groups
GV2, Trinity College Dublin
Design Graphics Lab, North Carolina State (Benjamin Watson)
Virtual Reality Lab, École Polytechnique Fédérale de Lausanne
Center for Human Modeling and Simulation, University of Pennsylvania
GAMMA group at University of North Carolina, Chapel Hill


GIS movement tracks

Big data movement analytics


climate indicators spatial analysis

Land indicators of climate

geosimulation high performance computing

High-performance computing and networking for geosimulation

earthquake model agent based GIS

Earthquake models

CA ice sheet model

Ice-sheet modeling

kinect control of GIS and robots
Robot motion control

simulating disasters ABM GIS
Human behavior in critical scenarios

crowd model riot model simulation wired

Modeling riots

physics engine GIS

Dynamic physics for built infrastructure

moving agents through space and time

Moving agents through space and time

validating agent based models

Validating agent-based models

machine learning GIS

Machine-learning behavioral geography

high performance computing urban simulation emergence

Accelerating agent-based models

megacity models

Megacity futures

immersive modeling

Immersive modeling

space-time GIS

Space-time GIS and analysis

measuring sprawl

A toolkit for measuring sprawl

space-time GIS

Modeling time, space, and behavior

simulating crowd behavior

Simulating crowd behavior

wi-fi geography

Wi-Fi geography

Simulating sprawl

Simulating sprawl