# Reconstructing commuters network using machine learning and urban indicators

arXiv: https://arxiv.org/abs/1908.03512 · HTML: https://mlsanity.com/paper/1908.03512/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting](https://mlsanity.com/paper/2310.18948/) (2023)
- [Human mobility is well described by closed-form gravity-like models learned automatically from data](https://mlsanity.com/paper/2312.11281/) (2023)
- [Origin-Destination Network Generation via Gravity-Guided GAN](https://mlsanity.com/paper/2306.03390/) (2023)
- [Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models](https://mlsanity.com/paper/2401.13098/) (2024)
- [Generating Origin-Destination Matrices in Neural Spatial Interaction Models](https://mlsanity.com/paper/2410.07352/) (2024)
- [From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics*](https://mlsanity.com/paper/2206.01176/) (2022)
- [Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents](https://mlsanity.com/paper/2308.16599/) (2023)
- [An Explainable Machine Learning Framework for Railway Predictive Maintenance using Data Streams from the Metro Operator of Portugal](https://mlsanity.com/paper/2508.05388/) (2025)
- [MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories](https://mlsanity.com/paper/2606.17978/) (2026)

## Abstract (arXiv)

Human mobility has a significant impact on several layers of society, from infrastructural planning and economics to the spread of diseases and crime. Representing the system as a complex network, in which nodes are assigned to regions (e.g., a city) and links indicate the flow of people between two of them, physics-inspired models have been proposed to quantify the number of people migrating from one city to the other. Despite the advances made by these models, our ability to predict the number of commuters and reconstruct mobility networks remains limited. Here, we propose an alternative approach using machine learning and 22 urban indicators to predict the flow of people and reconstruct the intercity commuters network. Our results reveal that predictions based on machine learning algorithms and urban indicators can reconstruct the commuters network with 90.4% of accuracy and describe 77.6% of the variance observed in the flow of people between cities. We also identify essential features to recover the network structure and the urban indicators mostly related to commuting patterns. As previously reported, distance plays a significant role in commuting, but other indicators, such as Gross Domestic Product (GDP) and unemployment rate, are also driven-forces for people to commute. We believe that our results shed new lights on the modeling of migration and reinforce the role of urban indicators on commuting patterns. Also, because link-prediction and network reconstruction are still open challenges in network science, our results have implications in other areas, like economics, social sciences, and biology, where node attributes can give us information about the existence of links connecting entities in the network.