000009818 001__ 9818
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000009818 0247_ $$2DOI$$a10.1016/j.physa.2009.12.015
000009818 0247_ $$2WOS$$aWOS:000275613800013
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000009818 041__ $$aeng
000009818 082__ $$a500
000009818 084__ $$2WoS$$aPhysics, Multidisciplinary
000009818 1001_ $$0P:(DE-Juel1)132269$$aSteffen, B.$$b0$$uFZJ
000009818 245__ $$aMethods for measuring pedestrian density, flow, speed and direction with minimal scatter
000009818 260__ $$aAmsterdam$$bNorth Holland Publ. Co.$$c2010
000009818 300__ $$a1902 - 1910
000009818 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
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000009818 3367_ $$2BibTeX$$aARTICLE
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000009818 440_0 $$04906$$aPhysica A$$v389$$x0378-4371$$y9
000009818 500__ $$aThe experiments are supported by the DFG under grant KL 1873/1-1 and SE 1789/1-1. We thank M. Boltes for his support in preparation of videos for analysis.
000009818 520__ $$aThe progress of image processing during recent years allows the measurement of pedestrian characteristics on a "microscopic" scale with low costs. However, density and flow are concepts of fluid mechanics defined for the limit of infinitely many particles. Standard methods of measuring these quantities locally (e.g. counting heads within a rectangle) suffer from large data scatter. The remedy of averaging over large spaces or long times reduces the possible resolution and inhibits the gain obtained by the new technologies.In this contribution we introduce a concept for measuring microscopic characteristics on the basis of pedestrian trajectories. Assigning a personal space to every pedestrian via a Voronoi diagram reduces the density scatter. Similarly, calculating direction and speed from position differences between times with identical phases of movement gives low-scatter sequences for speed and direction. Finally we discuss the methods to obtain reliable values for derived quantities and new possibilities of an in-depth analysis of experiments. The resolution obtained indicates the limits of stationary state theory. (C) 2009 Elsevier B.V. All rights reserved.
000009818 536__ $$0G:(DE-Juel1)FUEK411$$2G:(DE-HGF)$$aScientific Computing (FUEK411)$$cFUEK411$$x0
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000009818 588__ $$aDataset connected to Web of Science
000009818 65320 $$2Author$$aVideo tracking
000009818 65320 $$2Author$$aVoronoi diagram
000009818 65320 $$2Author$$aPedestrian modeling
000009818 65320 $$2Author$$aVelocity measurement
000009818 65320 $$2Author$$aPedestrian density
000009818 650_7 $$2WoSType$$aJ
000009818 7001_ $$0P:(DE-Juel1)132266$$aSeyfried, A.$$b1$$uFZJ
000009818 773__ $$0PERI:(DE-600)1466577-3$$a10.1016/j.physa.2009.12.015$$gVol. 389, p. 1902 - 1910$$p1902 - 1910$$q389<1902 - 1910$$tPhysica / A$$v389$$x0378-4371$$y2010
000009818 8567_ $$uhttp://dx.doi.org/10.1016/j.physa.2009.12.015
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000009818 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
000009818 9141_ $$y2010
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