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An Agent-Based Model for Estimating Human Activity Patterns on the Wasatch Front

By Albert M. Lund1,2, Nicole B. Burnett2,3, Ramkiran Gouripeddi1,2, and Julio C. Facelli1,2

1Department of Biomedical Informatics, 2Center for Clinical and Translational Science, 3Department of Chemistry

It is difficult to measure the impact of air quality on human health because populations are mobile. Additionally, air quality data is reported at low geographic resolutions (> 1 km2), which makes it difficult to characterize acute local variations in air quality. There are few examples of combining human movement and activity data with high resolution air quality data to capture trajectory based exposure profiles in a comprehensive way. An agent-based model helps simulate human activities and locations throughout an arbitrary day. Simulation is used to overcome the limitations of existing datasets; simulated households based on aggregate data for the state of Utah are modeled and activity profiles generated from the American Time Use Survey of the U.S. Bureau of Labor Statistics. The activity profiles are combined with the simulated households to build individual trajectories of activity and location over the desired region of study.

System Status

General Environment

last update: 2018-04-21 14:33:03
General Nodes
system cores % util.
ember 900/900 100%
kingspeak 880/880 100%
notchpeak 576/576 100%
lonepeak 980/1112 88.13%
Owner/Restricted Nodes
system cores % util.
ash 4796/6600 72.67%
notchpeak 448/480 93.33%
ember 1044/1220 85.57%
kingspeak 5904/6672 88.49%
lonepeak 380/400 95%

Protected Environment

last update: 2018-04-21 14:30:32
General Nodes
system cores % util.
redwood 0/500 0%
Owner/Restricted Nodes
system cores % util.
redwood 416/1680 24.76%

Cluster Utilization

Last Updated: 4/19/18