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000824905 005__ 20210129225142.0
000824905 0247_ $$2doi$$a10.1007/978-3-319-33482-0_10
000824905 037__ $$aFZJ-2016-07407
000824905 1001_ $$0P:(DE-Juel1)161133$$aLiao, Weichen$$b0$$eCorresponding author
000824905 1112_ $$aTraffic and Granular Flow$$cDelft$$d2015-10-28 - 2015-10-30$$gTGF15$$wNeederlands
000824905 245__ $$aDetection of Steady State in Pedestrian Experiments
000824905 260__ $$aCham$$bSpringer International Publishing$$c2016
000824905 29510 $$aTraffic and Granular Flow '15 / Knoop, Victor L. (Editor), Chapter 10 ; ISBN: 978-3-319-33481-3 
000824905 300__ $$a73 - 79
000824905 3367_ $$2ORCID$$aCONFERENCE_PAPER
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000824905 520__ $$aInitial conditions could have strong influences on the dynamics of pedestrian experiments. Thus, a careful differentiation between transient state and steady state is important and necessary for a thorough study. In this contribution a modified CUSUM algorithm is proposed to automatically detect steady state from time series of pedestrian experiments. Major modifications on the statistics include introducing a step function to enhance the sensitivity, adding a boundary to limit the increase, and simplifying the calculation to improve the computational efficiency. Furthermore, the threshold of the detection parameter is calibrated using an autoregressive process. By testing the robustness, the modified CUSUM algorithm is able to reproduce identical steady state with different references. Its application well contributes to accurate analysis and reliable comparison of experimental results.
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000824905 7001_ $$0P:(DE-Juel1)159135$$aTordeux, Antoine$$b1
000824905 7001_ $$0P:(DE-Juel1)132266$$aSeyfried, Armin$$b2
000824905 7001_ $$0P:(DE-Juel1)132077$$aChraibi, Mohcine$$b3
000824905 7001_ $$0P:(DE-HGF)0$$aZheng, Xiaoping$$b4
000824905 7001_ $$0P:(DE-HGF)0$$aZhao, Ying$$b5
000824905 773__ $$a10.1007/978-3-319-33482-0_10
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000824905 9141_ $$y2016
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