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Scalable Detection of Traffic Congestion from Massive Floating Car Data Streams
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1164-8403
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-5361-6034
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Motivated by the high utility and growing availability of Floating Car Data (FCD) streams for traffic congestion modeling and subsequent traffic congestion-related intelligent traffic management tasks, this paper proposes a grid-based, time-inhomogeneous model and method for the detection of congestion from large FCD streams. Furthermore, the paper proposes a simple but effective, high-level implementation of the method using off-the-shelf relational database technology that can readily be ported to Big Data processing frameworks. Empirical evaluations on millions of real-world taxi trajectories show that 1) the spatio-temporal distribution and clustering of the detected congestions are reasonable and 2) the method and its prototype implementation scale linearly with the input size and the geographical level of detail / spatio-temporal resolution of the model.

Place, publisher, year, edition, pages
ACM Press, 2015.
Keyword [en]
Congestion Detection, FCD, Trajectory Data Mining, Intelligent Transport Systems
National Category
Computer Science Information Systems
Research subject
Computer Science; Transport Science; Geodesy and Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-184250Scopus ID: 2-s2.0-84980396581ISBN: 978-1-4503-3973-5/15/11 (print)OAI: oai:DiVA.org:kth-184250DiVA: diva2:916036
Conference
The First International Workshop on Smart Cities and Urban Analytics (UrbanGIS) 2015, Bellevue, WA, USA, NOVEMBER 3, 2015
Note

QC 20160407

Available from: 2016-03-31 Created: 2016-03-31 Last updated: 2016-10-12Bibliographically approved

Open Access in DiVA

No full text

Other links

Scopushttp://engineering.nyu.edu/urbangis2015/

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Gidofalvi, GyözöYang, Can

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf