Scalable Detection of Traffic Congestion from Massive Floating Car Data Streams
2015 (English)Conference paper (Refereed)
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.
Congestion Detection, FCD, Trajectory Data Mining, Intelligent Transport Systems
Computer Science Information Systems
Research subject Computer Science; Transport Science; Geodesy and Geoinformatics
IdentifiersURN: urn:nbn:se:kth:diva-184250ISBN: 978-1-4503-3973-5/15/11OAI: oai:DiVA.org:kth-184250DiVA: diva2:916036
The First International Workshop on Smart Cities and Urban Analytics (UrbanGIS) 2015, Bellevue, WA, USA, NOVEMBER 3, 2015