A review of urban computing for mobile phone traces: current methods, challenges and opportunities

By Shan Jiang, Gaston A. Fiore, Yingxiang Yang, Joseph Ferreira Jr., Emilio Frazzoli, Marta C. González. 2013

298 citations recorded by the source corpus.

In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.

Subfields: Transportation, Signal Processing

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Citations in the corpus

A Survey on Deep Learning for Human Mobility (2020, background): “This deluge of digital data fostered a vast scientific production on various aspects of human mobility, such as the mining of trajectory data [88, 123, 198, 232, 236], the uncovering of the statistical patterns [11, 20, 68, 69, 176], and the estimation of the privacy risk [48, 62, 143, 144, 146, 158”
The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, methodology): “In other cases, those intermediate data points were not used; rather an incremental traffic assignment technique was applied (Jiang et al., 2013).”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, background): “At the level of the individual, daily trip chains/trajectories constructed from mobile phone data are consistent with household surveys (Jiang et al., 2013; Schneider et al., 2013).”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, methodology): “A similar approach was also used in (Jiang et al., 2013).”
The TimeGeo modeling framework for urban mobility without travel surveys (2016, methodology): “We apply the stay extraction method discussed in the literature (23) to both data sets.”
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, methodology): “Through more careful treatment of stay locations on fine-grained triangulated mobile phone CDR data for one million users in Boston, Jiang et al. [24] apply a similar approach to extract human daily motifs.”
Human mobility: Models and applications (2017, methodology): “Several algorithms in the literature are reviewed in [98], and further successive refinements and improvements in the methodology have been made since [95, 90, 100, 97].”
Tackling Climate Change with Machine Learning (2019, background): “[332] Shan Jiang, Gaston A Fiore, Yingxiang Yang, Joseph Ferreira Jr, Emilio Frazzoli, and Marta C González.”
Understanding Human Mobility from Twitter (2014, background): “To explore the heterogeneity of mobility among individuals, we study the radius of gyration rg, which is another important characteristic of human mobility that quantifies the spatial stretch of an individual trajectory or the traveling scale of an individual [7, 8].”