Netherlands

Netherlands

Crowds inside out: Understanding crowds from the perspective of individual crowd members’ experiences

Li, J. (TU Delft Human Information Communication Design)

With the growth of global population, the big cities become increasingly crowded. It is not rare to see large crowds in public transportations and events with masses of visitors, such as music festivals and football matches. The question “How to deal with crowds” is receiving attention, both from academia and practical crowd management.This thesis aims at contributing to a better understanding of crowds from the perspective of individual crowd members’ experiences, including their well- being, emotional experiences and action tendencies. In addition, we want to understand the emotional contagion effect between groups in crowds. To achieve this, we chose to go into the crowds, get in touch with the crowd members, and try to find out what factors sustain their well-being, how their emotional experiences can be measured in a playful and non-intrusive manner, what they tend to do when they have certain emotions, and how the grouping behavior reflects their experiences.

Estimate Sentiment of Crowds from Social Media during City Events

Gong, X. (TU Delft Transport and Planning)
Daamen, W. (TU Delft Transport and Planning)
Bozzon, A. (TU Delft Internet of Things)
Hoogendoorn, S.P. (TU Delft Transport and Planning

City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sentiments of crowds in city events. First, some lexicon-based and machine learning-based methods were selected to perform sentiment analyses, then an event-based sentiment annotated dataset was constructed. The performance of the selected methods was trained and tested in an experiment using common and event-based datasets. Results show that the machine learning method LinearSVC achieves the lowest estimation error for sentiment analysis on social media in city events. The proposed event-based dataset is essential for training methods to reduce estimation error in such contexts.

Using Social Media for Attendees Density Estimation in City-Scale Events

Gong, X. (TU Delft Transport and Planning)
Yang, J. (University of Fribourg)
Daamen, W. (TU Delft Transport and Planning)
Bozzon, A. (TU Delft Web Information Systems)
Hoogendoorn, S.P. (TU Delft Transport and Planning)
Houben, G.J.P.M. (TU Delft Web Information Systems)

City-scale events attract large amounts of attendees in temporarily re-purposed urban environments. In this setting, the real-time measurement of the density of attendees stationing in – or moving through – the event terrain is central to applications such as crowd management, emergency support, and quality of service evaluation. Sensing or communication infrastructures (e.g. sensor networks, mobile phones) can be deployed to estimate the number of attendees currently occupying an area. However, the adoption of these technologies is hindered by their cost or sensing resolution. There is evidence that social media data can provide a real-time and semantically rich insight into attendees’ behaviour during city-scale events. Their suitability as a data source for attendees density estimation is yet to be investigated. With this paper we aim at filling this knowledge gap by studying how micro-posts harvested from social media can be used during city-scale events to estimate the density of attendees in a given terrain. To cope with issues of temporal and spatial resolution, we propose 3 classes of density estimation strategies, i.e. geo-based, speed-based and flow-based strategy, inspired by elements of pedestrian traffic flow theory that were successfully assessed during city-scale events. We study the performance of these strategies in the context of SAIL Amsterdam 2015 (Sail) and Kingsday Amsterdam 2016 (Kingsday), two city-scale events that attracted 2 and 1.5 million of attendees in the span of 5 days and 1 day, respectively. We defined four experimental terrains for the Sail event and one for the Kingsday event, and compare density estimates from social media data with measures obtained from counting systems and Wi-Fi sensors. Results show the potential of solutions embedding elements from pedestrian traffic flow theory, which yielded estimates with strong temporal correlations with the sensor observation, and limited mean errors.

Analysis and Modelling of Pedestrian Movement Dynamics at Large-scale Events

Duives, D.C. (TU Delft Transport and Planning)

To what extent can we model the movements of pedestrians who walk across a large-scale event terrain? This dissertation answers this question by analysing the operational movement dynamics of pedestrians in crowds at several large music and sport events in the Netherlands and extracting the key crowd movement phenomena. A conceptual model and an assessment framework for pedestrian simulation models are developed specifically to describe and simulate this type of movement dynamics.

Sports crowd violence: An interdisciplinary synthesis

Ramón Spaaij

A landscape of crowd-management support: an integrative approach

NandaWijermansClaudineConradoMaartenvan SteenClaudioMartellaJieLi

Analysing the Effectiveness of Wearable Wireless Sensors in Controlling Crowd Disasters

Teo Yu Hui Angela, Vaisagh Viswanathan, Michael Lees, and Wentong Cai

Designing for Crowd Well-Being: Current Designs, Strategies and Future Design Suggestions

Jie Li, Huib de Ridder, Arnold Vermeeren, Claudine Conrado, Claudio Martella

Introduction to Crowd Science 

Prof. Dr. G. Keith Still FIMA FICPEM SFIIRSM MEWI FIPM FHEA, Capt. Marcel Altenburg MA PGDip

National Counterterrorism Strategy for 2016-2020

HEV 2018: procesmodel evenementenveiligheid

HEV 2018: procesmodel evenementenveiligheid

01 en 02 versie 2011.10.01 Handreiking Evenementenveiligheid2011, deel I en II, versie 1.0

03 versie 2011.11.22 Handreiking Evenementenveiligheid 2011, deel III, versie 1.0

04 versie 2011.11.08 Handreiking Evenementenveiligheid 2011, deel IV, versie 0.9, Evenement Assistent

Crisiscommunicatietips voor paniek in menigte (vluchtende mensenmassa)1.

Instituut Fysieke Veiligheid

Crowd Control en Smart Mobs Zelforganisatie en burgerparticipatie bij evenementen: een verkenning

Anneke van Hoek
Paul van Soomeren

VOORBEELD VEILIGHEIDSPLAN

HOE MOET EEN VEILIGHEIDSPLAN OPGESTELD WORDEN?

houten.nl

Format Veiligheidsplan Evenementenorganisatie

Bijlage D van Regionaal Evenementenbeleid
Oktober 2016

venray.nl

Inzet Communicatie bij Crowd Management en Crowd Control

Auteurs:
Dr. ir. Peter W. de Vries
Dr. Mirjam Galetzka
Dr. Jan M. Gutteling

Een gemeenschappelijk denkkader omtrent veiligheid
NEDERLANDS HANDBOEK EVENEMENTEN VEILIGHEID 1.0


bundeling van kennis, regelgeving, normen en ervaringen

Meer aandacht nodig voor veiligheid en gezondheid bij publieksevenementen
Rapport

rijksoverheid.nl

RAPPORT DIGITALE VEILIGHEID EVENEMENTEN

18 JUNI 2018
Etrit Asllani
Anouk van den Berg
Elly Hofman
Lu Xue

Regionaal kader evenementenveiligheid Samen voor veilige evenementen in Twente

Afwijkend Gedrag Maatschappelijk verantwoord waarnemen van gedrag in context van veiligheid – Tweede herziene druk

Jeroen van Rest

Maaike Roelofs

Anna van Nunen

Regionale multidisciplinaire leidraad veiligheid publieksevenementen Veiligheidsregio Groningen

Versie april 2012

“45e PINKPOP: SUPER EDITIE MET LEERMOMENTEN VOOR DE TOEKOMST”

RAPPORT,
INTERNE EVALUATIE
PINKPOP 2014
19 NOVEMBER 2014

Ongeüniformeerde private beveiligers in het publieke domein bij evenementen: een verkenning bij de Ziggo Dome in Amsterdam


Definitief rapport

Randy Bloeme, Paul van Soomeren & Bianca Szytniewski

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