# Network-Level Measures of Mobility from Aggregated Origin-Destination Data

arXiv: https://arxiv.org/abs/2502.04162 · HTML: https://mlsanity.com/paper/2502.04162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data](https://mlsanity.com/paper/1702.06270/) (2017)
- [Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics](https://mlsanity.com/paper/2209.12095/) (2022)
- [The NetMob2024 Dataset: Population Density and OD Matrices from Four LMIC Countries](https://mlsanity.com/paper/2410.00453/) (2024)
- [Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data](https://mlsanity.com/paper/1408.5420/) (2014)
- [Towards matching user mobility traces in large-scale datasets](https://mlsanity.com/paper/1709.05772/) (2017)
- [Understanding Human Mobility Flows from Aggregated Mobile Phone Data](https://mlsanity.com/paper/1803.00814/) (2018)

## Abstract (arXiv)

We introduce a framework for defining and interpreting collective mobility measures from spatially and temporally aggregated origin--destination (OD) data. Rather than characterizing individual behavior, these measures describe properties of the mobility system itself: how network organization, spatial structure, and routing constraints shape and channel population movement. In this view, aggregate mobility flows reveal aspects of connectivity, functional organization, and large-scale daily activity patterns encoded in the underlying transport and spatial network. To support interpretation and provide a controlled reference for the proposed time-elapsed calculations, we first employ an independent, network-driven synthetic data generator in which trajectories arise from prescribed system structure rather than observed data. This controlled setting provides a concrete reference for understanding how the proposed measures reflect network organization and flow constraints. We then apply the measures to fully anonymized data from the NetMob 2024 Data Challenge, examining their behavior under realistic limitations of spatial and temporal aggregation. While such data constraints restrict dynamical resolution, the resulting metrics still exhibit interpretable large-scale structure and temporal variation at the city scale.