Reconstructing commuters network using machine learning and urban indicators.

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 algori

SUBMITTER: Spadon G 

PROVIDER: S-EPMC6692407 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature