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A Model for Automatic Extraction of Slowdowns From Traffic Sensor Data

Paun, S and Kruschwitz, U and Poesio, M (2014) A Model for Automatic Extraction of Slowdowns From Traffic Sensor Data. In: 15th Annual PostGraduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting (PGNet 2014), ? - ?, Liverpool.


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The ability to identify slowdowns from a stream of traffic sensor readings in an automatic fashion is a core building block for any application which incorporates traffic behaviour into its analysis process. The methods proposed in this paper treat slowdowns as valley-shaped data sequences that are found below a normal distribution interval. This paper proposes a model for slowdown identification and partitioning across multiple periods of time and it aims to serve as a first layer of knowledge about the traffic environment. The model can be used to extract the regularities from a set of events of interest with recurring behaviour and to assert the consistency of the extracted patterns. The proposed methods are evaluated using real data collected from highway traffic sensors

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_ - Notes:
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 23 Jul 2015 11:36
Last Modified: 23 Sep 2022 19:15

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