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Urban Analytics of Big Transportation Data for Supporting Smart Cities

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Big Data Analytics and Knowledge Discovery (DaWaK 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11708))

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Abstract

The advances in technologies and the popularity of the smart city concepts have led to an increasing amount of digital data available for urban research, which in turn has led to urban analytics. In urban research, researchers who conduct paper-based or telephone-based travel surveys often collect biased and inaccurate data about movements of their participants. Although the use of global positioning system (GPS) trackers in travel studies improves the accuracy of exact participant trip tracking, the challenge of labelling trip purpose and transportation mode still persists. The automation of such a task would be beneficial to travel studies and other applications that rely on contextual knowledge (e.g., current travel mode of a person). In DaWaK 2018, we made use of both the GPS and accelerometer data to classify ground transportation modes. In the current DaWaK 2019 paper, we explore additional parameters—namely, dwell time and dwell time history (DTH)—to further enhance the urban analytic capability. In particular, with these additional parameters, classification and predictive analytics of ground transportation modes becomes more accurate. This, in turn, helps the development of a smarter city.

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Notes

  1. 1.

    https://impact.canada.ca/

  2. 2.

    https://impact.canada.ca/en/challenges/smart-cities, https://www.infrastructure.gc.ca/cities-villes/index-eng.html

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Acknowledgements

This project is partially supported by NSERC (Canada) and University of Manitoba.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Carson K. Leung, Peter Braun, Calvin S. H. Hoi & Joglas Souza

  2. University of Trieste, Trieste, TS, Italy

    Alfredo Cuzzocrea

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  1. Carson K. Leung
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  2. Peter Braun
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  3. Calvin S. H. Hoi
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  4. Joglas Souza
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  5. Alfredo Cuzzocrea
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Corresponding author

Correspondence to Carson K. Leung .

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Editors and Affiliations

  1. University of Houston, Houston, TX, USA

    Carlos Ordonez

  2. Drexel University, Philadelphia, PA, USA

    Il-Yeol Song

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Anderst-Kotsis

  4. Software Competence Center Hagenberg, Hagenberg im Mühlkreis, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Leung, C.K., Braun, P., Hoi, C.S.H., Souza, J., Cuzzocrea, A. (2019). Urban Analytics of Big Transportation Data for Supporting Smart Cities. In: Ordonez, C., Song, IY., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2019. Lecture Notes in Computer Science(), vol 11708. Springer, Cham. https://doi.org/10.1007/978-3-030-27520-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-27520-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27519-8

  • Online ISBN: 978-3-030-27520-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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