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Carlo Ratti

Researcher at Massachusetts Institute of Technology

Publications -  461
Citations -  24585

Carlo Ratti is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Mobile phone. The author has an hindex of 72, co-authored 415 publications receiving 19451 citations. Previous affiliations of Carlo Ratti include Singapore–MIT alliance & University of Cambridge.

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Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis

TL;DR: The ‘Mobile Landscapes' project is presented: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day, which enables a graphic representation of the intensity of urban activities and their evolution through space and time.
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Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

TL;DR: A new real-time urban monitoring system that marks the unprecedented monitoring of a large urban area, which covered most of the city of Rome, in real time using a variety of sensing systems and will hopefully open the way to a new paradigm of understanding and optimizing urban dynamics.
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Quantifying the benefits of vehicle pooling with shareability networks

TL;DR: The notion of shareability network is introduced, which allows to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets, and demonstrates the feasibility of a shareable taxi service in New York City.
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Geo-located Twitter as proxy for global mobility patterns

TL;DR: This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility and reveals spatially cohesive regions that follow the regional division of the world.
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Energy consumption and urban texture

TL;DR: In this article, the effects of urban texture on building energy consumption were explored based on the analysis of digital elevation models (DEMs), which have proven to be very effective in the urban context, and different algorithms were proposed and discussed, including the calculation of the urban surface-to-volume ratio and the identification of all building areas that are within 6 meters from a facade.