dorsal/arxiv
View SchemaScaling laws for the movement of people between locations in a large city
| Authors | Gerardo Chowell, James M. Hyman, Stephen Eubank, Carlos Castillo-Chavez |
|---|---|
| Categories | |
| ArXiv ID | physics/0503023 |
| URL | https://arxiv.org/abs/physics/0503023 |
| DOI | 10.1103/PhysRevE.68.066102 |
| Journal | Physical Review E 68, 066102 (2003) |
Abstract
Large scale simulations of the movements of people in a ``virtual'' city and their analyses are used to generate new insights into understanding the dynamic processes that depend on the interactions between people. Models, based on these interactions, can be used in optimizing traffic flow, slowing the spread of infectious diseases or predicting the change in cell phone usage in a disaster. We analyzed cumulative and aggregated data generated from the simulated movements of 1.6 million individuals in a computer (pseudo agent-based) model during a typical day in Portland, Oregon. This city is mapped into a graph with $181,206$ nodes representing physical locations such as buildings. Connecting edges model individual's flow between nodes. Edge weights are constructed from the daily traffic of individuals moving between locations. The number of edges leaving a node (out-degree), the edge weights (out-traffic), and the edge-weights per location (total out-traffic) are fitted well by power law distributions. The power law distributions also fit subgraphs based on work, school, and social/recreational activities. The resulting weighted graph is a ``small world'' and has scaling laws consistent with an underlying hierarchical structure. We also explore the time evolution of the largest connected component and the distribution of the component sizes. We observe a strong linear correlation between the out-degree and total out-traffic distributions and significant levels of clustering. We discuss how these network features can be used to characterize social networks and their relationship to dynamic processes.
{
"annotation_id": "eeafbb7d-a5b6-4d21-ae0c-c846b30ae324",
"date_created": "2026-03-02T18:00:56.934000Z",
"date_modified": "2026-03-02T18:00:56.934000Z",
"file_hash": "2e7fb9d7212b6f206ed3570b7689d85f653fe6fb3d70b4a63d273dc71534bb38",
"private": false,
"record": {
"abstract": "Large scale simulations of the movements of people in a ``virtual\u0027\u0027 city and\ntheir analyses are used to generate new insights into understanding the dynamic\nprocesses that depend on the interactions between people. Models, based on\nthese interactions, can be used in optimizing traffic flow, slowing the spread\nof infectious diseases or predicting the change in cell phone usage in a\ndisaster. We analyzed cumulative and aggregated data generated from the\nsimulated movements of 1.6 million individuals in a computer (pseudo\nagent-based) model during a typical day in Portland, Oregon. This city is\nmapped into a graph with $181,206$ nodes representing physical locations such\nas buildings. Connecting edges model individual\u0027s flow between nodes. Edge\nweights are constructed from the daily traffic of individuals moving between\nlocations. The number of edges leaving a node (out-degree), the edge weights\n(out-traffic), and the edge-weights per location (total out-traffic) are fitted\nwell by power law distributions. The power law distributions also fit subgraphs\nbased on work, school, and social/recreational activities. The resulting\nweighted graph is a ``small world\u0027\u0027 and has scaling laws consistent with an\nunderlying hierarchical structure. We also explore the time evolution of the\nlargest connected component and the distribution of the component sizes. We\nobserve a strong linear correlation between the out-degree and total\nout-traffic distributions and significant levels of clustering. We discuss how\nthese network features can be used to characterize social networks and their\nrelationship to dynamic processes.",
"arxiv_id": "physics/0503023",
"authors": [
"Gerardo Chowell",
"James M. Hyman",
"Stephen Eubank",
"Carlos Castillo-Chavez"
],
"categories": [
"physics.soc-ph"
],
"doi": "10.1103/PhysRevE.68.066102",
"journal_ref": "Physical Review E 68, 066102 (2003)",
"title": "Scaling laws for the movement of people between locations in a large city",
"url": "https://arxiv.org/abs/physics/0503023"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "1b8dc52a-ad8d-4ff8-bc88-e351eba3c0b2",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}