title: Understanding metropolitan patterns of daily encounters.
abstract: 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). 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.
authors.affiliation: Future Cities Laboratory, Singapore-ETH Centre for Global Environmental Sustainability, Singapore 138602.
authors.forename: Lijun
authors.initials: L
authors.lastname: Sun
authors.forename: Kay W
authors.initials: KW
authors.lastname: Axhausen
authors.forename: Der-Horng
authors.initials: DH
authors.lastname: Lee
authors.forename: Xianfeng
authors.initials: X
authors.lastname: Huang
mesh_terms: D002947:Cities; D006802:Human Activities / Q000706:statistics & numerical data*; D006801:Humans; D007398:Interpersonal Relations; D008962:Models, Theoretical; D018986:Motor Vehicles / Q000706:statistics & numerical data; D011157:Population Dynamics; D013997:Time Factors
keywords: behavioral rhythms; human mobility; social networks; social sciences
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