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Visual Analysis of Floating Taxi Data Based on Interconnected and Timestamped Area Selections

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Progress in Cartography

Part of the book series: Lecture Notes in Geoinformation and Cartography ((ICA))

Abstract

Floating Car Data (FCD) is GNSS-tracked vehicle movement, includes often large data size and is difficult to handle, especially in terms of visualization. Recently, FCD is often the base for interactive traffic maps for navigation and traffic forecasting. Handling FCD includes problems of large computational efforts, especially in case of connecting tracked vehicle positions to digitized road networks and subsequent traffic state derivations. Established interactive traffic maps show one possible visual representation for FCD. We propose a user-adapted map for the visual analysis of massive vehicle movement data. In our visual analysis approach we distinguish between a global and a local view on the data. Global views show the distribution of user-defined selection areas, in the way of focus maps. Local views show user-defined polygons with 2-D and 3-D traffic parameter visualizations and additional diagrams. Each area selection is timestamped with the time of its creation by the user. After defining a number of area selections it is possible to calculate weekday-dependent travel times based on historical taxi FCD. There are 3 different types of defined connections in global views. This has the aim to provide personalization for specific commuters by delivering only traffic and travel time information for and between user-selected areas. In a case study we inspect traffic parameters based on taxi FCD from Shanghai observed within 15 days in 2007. We introduce test selection areas, calculate their average traffic parameters and compare them with recent (2015) and typical traffic states coming from the Google traffic layer.

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Notes

  1. 1.

    http://geeknizer.com/how-google-maps-traffic-works/.

  2. 2.

    https://googleblog.blogspot.in/2009/08/bright-side-of-sitting-in-traffic.html.

  3. 3.

    http://wirelesslab.sjtu.edu.cn/taxi_trace_data.html.

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Acknowledgements

The described taxi Floating Car Data set of Shanghai (‘SUVnet-Trace Data’Footnote 3) was obtained from the Wireless and Sensor networks Lab (WnSN) at Shanghai Jiao Tong University. We would like to thank the Laboratory for Wireless and Sensor Networks at Shanghai Jiao Tong University, especially Prof. Min-You Wu and Jia Peng, for providing access to this data.

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Authors and Affiliations

  1. Institute of Geography, Applied Geoinformatics, University of Augsburg, Alter Postweg 118, 86159, Augsburg, Germany

    Andreas Keler & Jukka M. Krisp

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  1. Andreas Keler
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  2. Jukka M. Krisp
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Correspondence to Andreas Keler .

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Editors and Affiliations

  1. Inst Kartographie Geo-Medientechnik, Vienna University of Technology, Vienna, Austria

    Georg Gartner

  2. Vienna University of Technology, Vienna, Wien, Austria

    Markus Jobst

  3. Vienna University of Technology, Vienna, Wien, Austria

    Haosheng Huang

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Keler, A., Krisp, J.M. (2016). Visual Analysis of Floating Taxi Data Based on Interconnected and Timestamped Area Selections. In: Gartner, G., Jobst, M., Huang, H. (eds) Progress in Cartography. Lecture Notes in Geoinformation and Cartography(). Springer, Cham. https://doi.org/10.1007/978-3-319-19602-2_8

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