Network Origin-Destination Estimation using Percolation

This paper presents a network origin–destination estimation (NODE) method for trip distribution that applies a percolation-like graph search to allocate trips from origins to destinations. Expanding in cost order from each origin, NODE matches productions to available attractions, depleting destination capacities as they are filled. Multiple origins compete for the same destinations; later arrivals may be diverted to more distant alternatives. The result is an OD matrix spatially constrained by network topology and impedance, without a global gravity function or logit structure. NODE can replicate gravity results in some settings, but departs in cases of destination capacity constraints, network bottlenecks, and heterogeneous acceptance, offering a simple, network-aware alternative to conventional models.

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Publication Details

Journal
Findings
Published
2026-03-02
DOI
https://doi.org/10.32866/001c.147387
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
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article

Network Origin-Destination Estimation using Percolation

David Levinson
Findings
Human Mobility and Location-Based Analysis
article

Network Origin-Destination Estimation using Percolation

David Levinson
article en

Abstract

This paper presents a network origin–destination estimation (NODE) method for trip distribution that applies a percolation-like graph search to allocate trips from origins to destinations. Expanding in cost order from each origin, NODE matches productions to available attractions, depleting destination capacities as they are filled. Multiple origins compete for the same destinations; later arrivals may be diverted to more distant alternatives. The result is an OD matrix spatially constrained by network topology and impedance, without a global gravity function or logit structure. NODE can replicate gravity results in some settings, but departs in cases of destination capacity constraints, network bottlenecks, and heterogeneous acceptance, offering a simple, network-aware alternative to conventional models.

Findings
The University of Sydney (AU)
Openalex Percentile: Top 39%
Human Mobility and Location-Based Analysis
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