Evidence for a Conserved Quantity in Human Mobility
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Abstract (arXiv)
Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. A concurrent literature has emphasized the explorative nature of human behavior, showing that the number of visited places grows steadily over time. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyze high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25 locations. We use this finding to improve state-of-the-art modeling of human mobility. Furthermore, shifting the attention from aggregated quantities to individual behavior, we show that the size of an individual's set of preferred locations correlates with the number of her social interactions. This result suggests a connection between the conserved quantity we identify, which as we show can not be understood purely on the basis of time constraints, and the `Dunbar number' describing a cognitive upper limit to an individual's number of social relations. We anticipate that our work will spark further research linking the study of Human Mobility and the Cognitive and Behavioral Sciences.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Sep 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1; 2025: 2.
7 papers.
- 2023Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulationcited by 15
- 2024Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasauruscited by 8
- 2022Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamicscited by 5
- 2021PG2Net: Personalized and Group Preferences Guided Network for Next Place Predictioncited by 1
- 2023A causal intervention framework for synthesizing mobility data and evaluating predictive neural networkscited by 1
- 2025Deep Generative Model for Human Mobility Behaviorcited by 0
- 2025On Predicting Sociodemographics from Mobility Signalscited by 0
- 2025On Predicting Sociodemographics from Mobility Signals6 Nov 2025
- 2025Deep Generative Model for Human Mobility Behavior7 Oct 2025
- 2024Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus20 Sep 2024
- 2023A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks20 Nov 2023
- 2023Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation15 Jun 2023
- 2022Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics24 Sep 2022
- 2021PG2Net: Personalized and Group Preferences Guided Network for Next Place Prediction15 Oct 2021