Estimating Origin-Destination Flows Using Mobile Phone Location Data

By Francesco Calabrese, Giusy Di Lorenzo, Liang Liu, Carlo Ratti. IEEE pervasive computing, 2011

473 citations recorded by the source corpus.

Using an algorithm to analyze opportunistically collected mobile phone location data, the authors estimate weekday and weekend travel patterns of a large metropolitan area with high accuracy.

Subfields: Global and Planetary Change, Transportation

View Paper: https://www.semanticscholar.org/paper/ebe0a85e3e62f7e8e4a8c2517491c37277bfbfdf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

In Transportation

Citations in the corpus

Human mobility: Models and applications (2017, background): “Following this, a series of papers highlighted possibilities of real-time visualization and monitoring of displacements in cities [328,329,96].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, methodology): “This was one of the problems addressed in (Calabrese et al., 2011) when dealing with regional partitioning at the level of the entire USA.”
A Survey on Deep Learning for Human Mobility (2020, unknown): “, neighborhoods or municipalities) [27].”
Development of origin–destination matrices using mobile phone call data (2014, background): “(37) have proposed a methodology to 93 reduce the noise in the CDR data but both studies have focused more on computation issues and 94 the relationship between the mobile phone OD and the traffic OD have not been explored in 95”
Configuring participation: on how we involve people in design (2013, background): “…ways of distributing the decision making process within organizations or communities [14,15], to refer to audience involvement in the creation of a digital artwork [45,53], to discuss knowledge sharing on social networks [44,51,52] or unwittingly taking part in the collection of data [13,23].”
Inferring land use from mobile phone activity (2012, methodology): “Finally, CDR data have proven useful to detect movement at the census tract scale [3].”