Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

By Francesco Calabrese, Massimo Colonna, Piero Lovisolo, Dario Parata, Carlo Ratti. IEEE transactions on intelligent transportation systems (Print), 2011

644 citations recorded by the source corpus.

This paper describes a new real-time urban monitoring system. The system uses the Localizing and Handling Network Event Systems (LocHNESs) platform developed by Telecom Italia for the real-time evaluation of urban dynamics based on the anonymous monitoring of mobile cellular networks. In addition, data are supplemented based on the instantaneous positioning of buses and taxis to provide information about urban mobility in real time, ranging from traffic conditions to the movements of pedestrians throughout the city. This system was exhibited at the Tenth International Architecture Exhibition of the Venice Biennale. It 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.

Subfields: Electrical and Electronic Engineering, Global and Planetary Change, Transportation

View Paper: https://www.semanticscholar.org/paper/5ba34c0318e9c43133dae07a3a6fbc1770da5727

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Citations in the corpus

Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, background): “As wireless mobile connectivity has changed the way people communicate, work, and play, mobile phone data can be used to derive the spatiotemporal information of anonymous phone users’ whereabouts for analysis of their mobility patterns [14], [15], [16].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, methodology): “For example, Calabrese et al. [2010] fixed both a spatial Sth and a temporal Tth threshold in order to detect stops; that is, two consecutive stops stopi and stop j can be collapsed in the same stop if distance(dstopi , dstopi ) < Sth and (tstopi − tstopj ) > Tth. A similar approach was also used in”
The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, methodology): “If the location data are in triangulated and processed latitude and longitude positions (e.g., sightings data), aggregation to a pre-determined zone structure is required (e.g., grids, census tracts, or traffic analysis zones) (Calabrese et al., 2011).”
Data-Driven Intelligent Transportation Systems: A Survey (2011, neutral): “[64] used the real-time data collected from”
Mobile Crowd Sensing and Computing (2015, methodology): “By using data from buses, taxies, and mobile phones, the Real Time Rome project [Calabrese et al. 2011] could report real-time urban dynamics.”
Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2013, methodology): “To avoid this problem, we apply a low-pass filter to the data and resample every 10 min, following the approach proposed and evaluated in Rome (Calabrese and Ratti, 2006; Calabrese et al., 2011a).”