Human mobility is well described by closed-form gravity-like models learned automatically from data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915419042873344 |
|---|---|
| author | Cabanas-Tirapu, Oriol Danús, Lluís Moro, Esteban Sales-Pardo, Marta Guimerà, Roger |
| author_facet | Cabanas-Tirapu, Oriol Danús, Lluís Moro, Esteban Sales-Pardo, Marta Guimerà, Roger |
| contents | Modeling of human mobility is critical to address questions in urban planning and transportation, as well as global challenges in sustainability, public health, and economic development. However, our understanding and ability to model mobility flows within and between urban areas are still incomplete. At one end of the modeling spectrum we have simple so-called gravity models, which are easy to interpret and provide modestly accurate predictions of mobility flows. At the other end, we have complex machine learning and deep learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models at the cost of not being interpretable and not providing insight on human behavior. Here, we show that simple machine-learned, closed-form models of mobility are able to predict mobility flows more accurately, overall, than either gravity or complex machine and deep learning models. At the same time, these models are simple and gravity-like, and can be interpreted in terms similar to standard gravity models. Furthermore, these models work for different datasets and at different scales, suggesting that they may capture the fundamental universal features of human mobility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_11281 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Human mobility is well described by closed-form gravity-like models learned automatically from data Cabanas-Tirapu, Oriol Danús, Lluís Moro, Esteban Sales-Pardo, Marta Guimerà, Roger Physics and Society Social and Information Networks Data Analysis, Statistics and Probability Machine Learning Modeling of human mobility is critical to address questions in urban planning and transportation, as well as global challenges in sustainability, public health, and economic development. However, our understanding and ability to model mobility flows within and between urban areas are still incomplete. At one end of the modeling spectrum we have simple so-called gravity models, which are easy to interpret and provide modestly accurate predictions of mobility flows. At the other end, we have complex machine learning and deep learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models at the cost of not being interpretable and not providing insight on human behavior. Here, we show that simple machine-learned, closed-form models of mobility are able to predict mobility flows more accurately, overall, than either gravity or complex machine and deep learning models. At the same time, these models are simple and gravity-like, and can be interpreted in terms similar to standard gravity models. Furthermore, these models work for different datasets and at different scales, suggesting that they may capture the fundamental universal features of human mobility. |
| title | Human mobility is well described by closed-form gravity-like models learned automatically from data |
| topic | Physics and Society Social and Information Networks Data Analysis, Statistics and Probability Machine Learning |
| url | https://arxiv.org/abs/2312.11281 |