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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
https://doi.org/10.1016/b978-0...
Part of book or chapter of book . 2018 . Peer-reviewed
License: Elsevier TDM
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Big Data in Road Transport and Mobility Research

Authors: Campos Cordobés, Sergio; Del Ser, Javier; Laña, Ibai; Olabarrieta, Ignacio (Iñaki); Sánchez Cubillo, Javier; Sánchez-Medina, Javier J.; Torre-Bastida, Ana I.;

Big Data in Road Transport and Mobility Research

Abstract

Ubiquitous computing has changed the acquisition of mobility data, with two aspects contributing: the high penetration rate and the ability to capture and share information on a continuous basis. This applies to geolocation information, operational mobile phone data, and also, social network crowdsourced information. Additionally, under the umbrella of the Internet of Things trend, the deployment of the Connected Vehicle (Car-as-a-sensor) concept, supported by advanced V2X communications, provides massive data volume. For all these cases, data are open to never before seen opportunities to analyze and predict individual and aggregated mobility patterns. Big Data refers to the processsing capabilities of such an explosion in the amount, quality, and heterogeneity of available data. This chapter will review the most relevant data sources, introduce the underlying techniques supporting the BigData paradigm and, finally, provide a list of some relevant applications in the transport and mobility domain.

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Keywords

Data, Bigdata, 120304 Inteligencia artificial, Machine learning, Applications, FCD, Processing architectures, 120903 Análisis de datos

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Average
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