# Understanding metropolitan patterns of daily encounters

arXiv: https://arxiv.org/abs/1301.5979 · HTML: https://mlsanity.com/paper/1301.5979/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2013.

## Built on by (most cited first, 7 of 7)

- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [Context-aware multi-head self-attentional neural network model for next location prediction](https://mlsanity.com/paper/2212.01953/) (2022)
- [How do you go where? Improving next location prediction by learning travel mode information using transformers](https://mlsanity.com/paper/2210.04095/) (2022)
- [Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks](https://mlsanity.com/paper/2101.00307/) (2021)
- [Individual Mobility Prediction via Attentive Marked Temporal Point Processes](https://mlsanity.com/paper/2109.02715/) (2021)
- [Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities](https://mlsanity.com/paper/2505.15870/) (2025)
- [Encounter-Driven City-Scale Human Mobility Generation with Large Language Models](https://mlsanity.com/paper/2610.17672/) (2026)

## Abstract (arXiv)

Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). This is the first time that such a large network of encounters has been identified and analyzed. Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters' bounded nature. An individual's encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of "familiar strangers" in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or "structure of co-presence" across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and -- particularly -- disclosing the impact of human behavior on various diffusion/spreading processes.