Long-range dependence and time-clustering behavior in pedestrian movement patterns in stampedes: The Love Parade case-study

2017 ◽  
Vol 469 ◽  
pp. 265-274 ◽  
Author(s):  
Liping Lian ◽  
Weiguo Song ◽  
Yuen Kwok Kit Richard ◽  
Jian Ma ◽  
Luciano Telesca
2020 ◽  
Vol 57 (4) ◽  
pp. 1234-1251
Author(s):  
Shuyang Bai

AbstractHermite processes are a class of self-similar processes with stationary increments. They often arise in limit theorems under long-range dependence. We derive new representations of Hermite processes with multiple Wiener–Itô integrals, whose integrands involve the local time of intersecting stationary stable regenerative sets. The proof relies on an approximation of regenerative sets and local times based on a scheme of random interval covering.


Author(s):  
Jan Beran ◽  
Britta Steffens ◽  
Sucharita Ghosh

AbstractWe consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karen McCulloch ◽  
Nick Golding ◽  
Jodie McVernon ◽  
Sarah Goodwin ◽  
Martin Tomko

AbstractUnderstanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.


2006 ◽  
Vol 16 (18) ◽  
pp. 1331-1338 ◽  
Author(s):  
Christos Christodoulou-Volos ◽  
Fotios M. Siokis

2012 ◽  
Vol 105 (1) ◽  
pp. 322-347 ◽  
Author(s):  
Jan Beran ◽  
Yevgen Shumeyko

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