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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Reconstructing commuters network using machine learning and urban indicators

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Author(s):
Spadon, Gabriel [1] ; de Carvalho, Andre C. P. L. F. [1] ; Rodrigues-Jr, Jose F. ; Alves, Luiz G. A. [2, 3]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil
[2] Rodrigues-Jr, Jr., Jose F., Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil
[3] Northwestern Univ, Dept Chem & Biol Engn, Evanston, IL 60208 - USA
Total Affiliations: 3
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 9, AUG 13 2019.
Web of Science Citations: 0
Abstract

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. (AU)

FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
Grantee:Agma Juci Machado Traina
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/08376-0 - Analysis and improvement of urban systems using digital maps in the form of complex networks
Grantee:Gabriel Spadon de Souza
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 19/04461-9 - Advancing medical prognosis based on graph concepts and artificial neural networks
Grantee:Gabriel Spadon de Souza
Support type: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/16987-7 - A complex system approach to urban planning and development
Grantee:Luiz Gustavo de Andrade Alves
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 16/18615-0 - Advanced machine learning
Grantee:André Carlos Ponce de Leon Ferreira de Carvalho
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 14/25337-0 - Design of vertex-centric algorithms for pattern recognition on large-scale graphs using asynchronous parallel processing
Grantee:Gabriel Perri Gimenes
Support type: Scholarships in Brazil - Doctorate