Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data

By Santi Phithakkitnukoon, T. Horanont, Giusy Di Lorenzo, Ryosuke Shibasaki, Carlo Ratti. International Workshop on Human Behavior Unterstanding, 2010

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The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, methodology): “To derive the types of locations, researchers have used simplistic methods, such as the frequency-based approach (Phithakkitnukoon et al., 2010; Alexander et al., 2015), which assigns the most frequently visited locations during day time and night time as home and work locations, respectively, or th”
Representation learning for geospatial areas using large-scale mobility data from smart card (2016, background): “Modeling and predicting people flow in a specific area results in understanding the characteristics or demographics of the area by combining activity patterns of people with external information about the area [7].”
Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card (2016, background): “Modeling and predicting people flow in a specific area results in understanding the characteristics or roles of the area by combining activity patterns of people with external information about the area [8].”
Understanding intra-urban trip patterns from taxi trajectory data (2012, background): “Currently, location-aware devices are widely applied in urban studies (Chowell et al. 2003; Phithakkitnukoon et al. 2010; Ratti et al. 2006; Shoval 2008).”