Origin-destination trips by purpose and time of day inferred from mobile phone data

By Lauren P. Alexander, Shan Jiang, M. Murga, Marta C. González. 2015

634 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/159a3cda3a5510f86b5eccc00b784f1967c2a7b8

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In Transportation

Citations in the corpus

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”
Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal (2016, background): “The term paratransit conventionally described “those forms of intra-urban passenger transportation which are available to the public, and distinct from conventional transit (scheduled bus and rail) and can operate over the highway and transit system” [4].”
The path most traveled: Travel demand estimation using big data resources (2015, methodology): “Alexander et al. (2015) and Çolak et al. (2015) improve on methods by Wang et al. (2012) and Iqbal et al. (2014) by using visit frequencies and temporal data to infer contextual information such as a location’s function or trip purpose.”
Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, methodology): “Using triangulated mobile phone records of millions of anonymous users, authors of [113] present a method to predict average daily OD trips.”
Human mobility: Models and applications (2017, methodology): “Consequently, to circumvent these difficulties, a large body of literature has dealt with extracting OD matrices from recently available, individual digital footprints [91, 92, 93, 94, 95, 90, 96, 97, 98].”
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, methodology): “Inspired by the algorithm introduced by Hariharan and Toyama [40] which was tested previously using triangulated mobile phone data [24], [29], we modified the algorithm for the”
The TimeGeo modeling framework for urban mobility without travel surveys (2016, background): “By labeling visited location types for individual users as home, work, or other, representative traffic origin–destination (OD) matrices for an average day and by time of day can be generated (24, 25).”