Model highlights patterns in how humans move across different locations
Every day, billions of people travel from their homes to work, schools, health care facilities, restaurants, public venues and other destinations. The complex patterns that shape how people move between these different locations are broadly referred to as "human mobility."
A deeper understanding of human mobility can help engineers and policymakers improve urban environments, boost the efficiency of transportation systems and prepare adequately for unexpected emergencies, such as pandemics or natural disasters. Most existing human mobility models assume that people's movements follow similar rules, regardless of the distance traveled or their destination.
Researchers at Rensselaer Polytechnic Institute recently set out to model people's movements using travel trajectories derived from anonymized cellphone data. Their paper, published in the Journal of the Royal Society Interface, details the work.
"This is a sister paper to our earlier study, in which we summarized the main discoveries about a universal inflation law of human mobility," Jianxi Gao, senior author of the paper, told Phys.org.
"The present paper takes the next step by asking what underlying mechanism could generate the inflation law. The original idea came from a sermon at my church, where a visiting pastor showed us the picture View of the World from 9th Avenue.
"In this image, the area close to Ninth Avenue appears very large, while places farther away are compressed into a much smaller space. This led me to ask whether this was simply artistic perspective or whether it also reflected how people perceive space and time."
An ecology-inspired human mobility model
Gao and his colleagues initially set out to better understand how people perceive space and time, as well as how their perceptions might influence their mobility patterns. After careful investigation, they realized that people might not perceive space only in terms of physical distance, but also in terms of their relationships and connections to specific places.
The researchers subsequently tried to develop a mode-switching model of human mobility that might also account for people's perceptions of space. To create this model, they drew from theories rooted in ecology and biology suggesting that animals switch between different movement strategies depending on what they are trying to do.
"Previous mobility models usually focused on travel patterns between pairs of locations (for example, number of trips between two locations), but they often failed to reproduce network-level properties such as modularity and average shortest-path length," explained Gao.
"We analyzed two large-scale mobile-phone data sets. One consisted of six months of privacy-enhanced GPS trajectory data from approximately 2 million anonymized users in the United States. The other included two weeks of call-detail records from 300,000 anonymized users in Senegal."
Using these anonymized cellphone records, Gao and his colleagues reconstructed the movements of millions of people over time. Ultimately, they created large human mobility networks that mapped connections between different places or locations.
"We found that real-world mobility networks showed very different structures from those generated by the classic exploration and preferential return (EPR) model often employed in human mobility research," said Gao. "Our mode-switching model was able to capture these network-level patterns more accurately."
When they examined the human mobility networks they had created, the researchers realized that they appeared to be divided into distinct spatial modules or regions. The team showed that movements between locations within the same module and those that crossed into another module were linked to different behavioral patterns.
Next steps for human mobility research
This study offers new insight into human mobility that could potentially inform the future design of cities and larger urban regions, as well as improvements to public transportation systems. In the future, the team's switching mechanism could be used to explain how humans navigate clustered, resource-rich urban environments and design more equitable urban infrastructure.
"We believe that future human mobility research should not focus only on predicting pairwise travel patterns, but should also consider network-level mobility structures," said Gao.
"We showed that these structural differences can affect epidemic-spreading patterns. Future pandemic-control strategies may therefore need to account for movements between different activity regions. For example, people who live far from an outbreak location but frequently connect different regions may contribute to disease spread."
As part of their future studies, the researchers could try to validate scenario-specific predictions derived from their model using real-world data. Currently, they are conducting new research focusing on human mobility during different types of emergencies or natural disasters, including floods, hurricanes, wildfires and pandemics.
"The current modeling framework considers only two organizational levels: within-module and cross-module movements," said Gao. "We now plan to extend it to more general hierarchical structures.
"In addition, this paper explores a relational view of mobility in space, and I am also interested in developing a similar perspective on time. For example, when I lived in Boston, a local trip of one to two hours seemed reasonable. After moving to Troy, New York, a trip of 20 to 40 minutes began to feel like a normal local trip. This raises the question of how people's perception of travel time affects their mobility patterns."
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Publication details
Lu Zhong et al, Switching exploration modes in human mobility, Journal of the Royal Society Interface (2026). DOI: 10.1098/rsif.2025.0817.
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Citation: Model highlights patterns in how humans move across different locations (2026, July 15) retrieved 16 July 2026 from https://phys.org/news/2026-07-highlights-patterns-humans.html
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