Relevant bibliographies by topics / Pedestrian crowds

Academic literature on the topic 'Pedestrian crowds'

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Published: 6 September 2023

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Journal articles on the topic "Pedestrian crowds"

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Zhang, Dawei, Haitao Zhu, Shi Qiu, and Boyan Wang. "Characterization of Collision Avoidance in Pedestrian Crowds." Mathematical Problems in Engineering 2019 (March 28, 2019): 1–9. http://dx.doi.org/10.1155/2019/9237674.

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The avoidance behavior of pedestrians was characterized in the present paper by simulating the movement of crowds in both unidirectional and bidirectional pedestrian flow. A phase change of alternative lane formation observed in real bidirectional pedestrian flows has been studied, where pedestrians tended to evade individuals in counterflow and simultaneously keep a certain distance from each other in the uniform pedestrian flow when the counterflow disappeared. What is more, the comparison between the effect of evading and pushing behavior on evacuation has been investigated in the room egress scenario. Additionally, the evading and overtaking behavior of fast pedestrians have also been simulated in heterogeneous crowds. The performance of the proposed model was compared to the experimental data and the results obtained using other evacuation models. Numerical results showed that both the phase change of alternative lane formation in bidirectional pedestrian flow and the effective evading behavior in unidirectional pedestrian flow were conductive to reduce the evacuation time of pedestrian crowds. Even though pushing behavior of fast pedestrians seemed to improve the flow through the wide exit, it might lead to the panic and other negative effect on the crowds, such as crowds trample. The proposed model in this paper could provide a theoretical basis for the pedestrian crowd management during emergency evacuation.
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Appert-Rolland, Cécile, Julien Pettré, Anne-Hélène Olivier, William Warren, Aymeric Duigou-Majumdar, Etienne Pinsard, and Alexandre Nicolas. "Experimental Study of Collective Pedestrian Dynamics." Collective Dynamics 5 (September 11, 2020): A109. http://dx.doi.org/10.17815/cd.2020.109.

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We report on two series of experiments, conducted in the frame of two different collaborations designed to study how pedestrians adapt their trajectories and velocities in groups or crowds. Strong emphasis is put on the motivations for the chosen protocols and the experimental implementation. The first series deals with pattern formation, interactions between pedestrians, and decision-making in pedestrian groups at low to medium densities. In particular, we show how pedestrians adapt their headways in single-file motion depending on the (prescribed) leader’s velocity. The second series of experiments focuses on static crowds at higher densities, a situation that can be critical in real life and in which the pedestrians’ choices of motion are strongly constrained sterically. More precisely, we study the crowd’s response to its crossing by a pedestrian or a cylindrical obstacle of 74cm in diameter. In the latter case, for a moderately dense crowd, we observe displacements that quickly decay with the minimal distance to the obstacle, over a lengthscale of the order of the meter.
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Li, Maoyu, Zhizuan Zhou, Ping Zhang, Nan Jiang, Xinmiao Jia, Xiaoyu Ju, and Lizhong Yang. "Effect of walking height on movement of individuals and crowds in a corridor." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 8 (August 1, 2023): 083403. http://dx.doi.org/10.1088/1742-5468/aceb59.

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Abstract The safe and efficient evacuation of pedestrians in an emergency has been widely investigated in the past. Stooping is a common walking posture of pedestrians in evacuation. Understanding the basic laws of pedestrian movement with stoop posture is instructive for developing effective evacuation management in an emergency. However, the evacuation behaviors and movement characteristics of stooped pedestrians are still unclear. In this study, a series of experiments on individuals and crowds with different walking heights were conducted in an 18 m long corridor. It was found that a left-deflection phenomenon appears in the transition from upright walking to stooped walking, and the deflection angles of pedestrians decrease with increasing walking height. The influence of walking height on pedestrian velocity is revealed through the comparison of individual and crowd movement. Restricted walking height can promote the movement velocity of individuals at low speed levels, and weakens the velocity at high speed levels. In contrast to the individual, the velocities of crowds at different speed levels will be enhanced by restricted walking height. Generally, pedestrian movement is affected by the initial motivation, walking height and pedestrian density. Restricted walking height has an encouraging effect in the motivation of pedestrian movement but can increase the physical discomfort of pedestrians. Besides, crowd density has an inhibitory effect on pedestrian movement, and restricted walking height can alleviate high crowd density, implying that appropriate stoop movement can facilitate pedestrian traffic and evacuation to some extent.
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Templeton, Anne, John Drury, and Andrew Philippides. "Walking together: behavioural signatures of psychological crowds." Royal Society Open Science 5, no. 7 (July 2018): 180172. http://dx.doi.org/10.1098/rsos.180172.

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Research in crowd psychology has demonstrated key differences between the behaviour of physical crowds where members are in the same place at the same time, and the collective behaviour of psychological crowds where the entire crowd perceive themselves to be part of the same group through a shared social identity. As yet, no research has investigated the behavioural effects that a shared social identity has on crowd movement at a pedestrian level. To investigate the direction and extent to which social identity influences the movement of crowds, 280 trajectories were tracked as participants walked in one of two conditions: (1) a psychological crowd primed to share a social identity; (2) a naturally occurring physical crowd. Behaviour was compared both within and between the conditions. In comparison to the physical crowd, members of the psychological crowd (i) walked slower, (ii) walked further, and (iii) maintained closer proximity. In addition, pedestrians who had to manoeuvre around the psychological crowd behaved differently to pedestrians who had to manoeuvre past the naturally occurring crowd. We conclude that the behavioural differences between physical and psychological crowds must be taken into account when considering crowd behaviour in event safety management and computer models of crowds.
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Bellomo, Nicola, and Livio Gibelli. "Toward a mathematical theory of behavioral-social dynamics for pedestrian crowds." Mathematical Models and Methods in Applied Sciences 25, no. 13 (September 17, 2015): 2417–37. http://dx.doi.org/10.1142/s0218202515400138.

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This paper presents a new approach to behavioral-social dynamics for pedestrian crowds by suitable development of mathematical tools of the kinetic theory. It is shown how pedestrians heterogeneity and the propagation of local unusual behaviors in the crowd can be accounted for. The proposed model is applied to the study of two groups of pedestrians walking in opposite directions in a crowded street and its predictive ability is demonstrated by showing that emerging behaviors, such as pedestrian segregation, can be depicted.
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Qiu, Fasheng, and Xiaolin Hu. "A Framework for Modeling Social Groups in Agent-Based Pedestrian Crowd Simulations." International Journal of Agent Technologies and Systems 4, no. 1 (January 2012): 39–58. http://dx.doi.org/10.4018/jats.2012010103.

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Grouping is a common phenomenon in pedestrian crowds and social groups can have significant impacts on crowd behavior. Despite its importance, how to model social groups in pedestrian crowd simulations is still an open and challenging issue. This paper presents a framework for modeling social groups in agent-based pedestrian crowd simulations. The developed framework integrates agent behavior modeling, group modeling, and social context modeling in a layered architecture, where each layer focuses on modeling a specific aspect of pedestrian crowds. A model of dynamic grouping behavior is developed to demonstrate the utility of the developed framework, and experimental results are presented.
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Li, Liang, Hong Liu, and Yanbin Han. "An approach to congestion analysis in crowd dynamics models." Mathematical Models and Methods in Applied Sciences 30, no. 05 (April 16, 2020): 867–90. http://dx.doi.org/10.1142/s0218202520500177.

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This paper presents a novel approach to quantitatively analyzing pedestrian congestion in evacuation management based on the Hughes and social force models. An accurate analysis of crowds plays an important role in illustrating their dynamics. However, the majority of the existing approaches to analyzing pedestrian congestion are qualitative. Few methods focus on the quantification of the interactions between crowds and individual pedestrians. According to the proposed approach, analytic tools derived from theoretical mechanics are applied to provide a multiscale representation of such interactions. In particular, we introduce movement constraints that illustrate the macroscopic and microscopic states of crowds. Furthermore, we consider pressure propagation and changes in the position of pedestrians during the evacuation process to improve the accuracy of the analysis. The generalized force caused by the varied density of pedestrians is applied to calculate the final congestion. Numerical simulations demonstrate the validity of the proposed approach.
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da Silva, Felipe Tavares, Halane Maria Braga Fernandes Brito, and Roberto Leal Pimentel. "Modeling of crowd load in vertical direction using biodynamic model for pedestrians crossing footbridges." Canadian Journal of Civil Engineering 40, no. 12 (December 2013): 1196–204. http://dx.doi.org/10.1139/cjce-2011-0587.

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In the analysis of vibration of footbridges in vertical direction, for crowd situations, there is evidence in the literature that the whole effect of pedestrian action is not well modeled when applying current force-only models to represent such an action. In these models, the action of each pedestrian is represented by a pulsating force applied on the structure. In this paper, a crowd load model is proposed for sparse and dense crowds (with densities up to around 1.0 pedestrian/m2) in which biodynamic models are added to represent the whole action of pedestrians. The focus of the investigation is on vibration effects in vertical direction. Comparisons with measurements on a prototype footbridge were carried out and made it possible to identify differences in the structural response when applying force-only and force-biodynamic models to represent the pedestrian action. The latter (proposed) model resulted in a better agreement with the measurements.
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Liao, Can, Kejun Zhu, Haixiang Guo, and Jian Tang. "Simulation Research on Safe Flow Rate of Bidirectional Crowds Using Bayesian-Nash Equilibrium." Complexity 2019 (January 15, 2019): 1–15. http://dx.doi.org/10.1155/2019/7942483.

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Current research on pedestrian flows has mainly focused on evacuation optimization during or after emergencies; however, crowd management before emergencies has received little attention. This paper examines the management of a Safe Pedestrian Flow Rate, in which the Bayesian-Nash Equilibrium mimics pedestrians’ decision-making, and a multiagent system is employed to reproduce pedestrians’ interactions. In the model, the pedestrian tunnel is divided into cells, with each pedestrian in a cell receiving a utility depending on the distance to the exit and the number of pedestrians in the cell. Then, each pedestrian uses the Bayesian-Nash Equilibrium to search for the target cell with maximum expected utility, moves in, and makes next decision until exiting the tunnel. The simulation model is calibrated and validated from a real scenario. Finally, from the experimental data collected from different simulation scenarios, this research reaches the conclusion that the Safe Pedestrian Flow Rate increases by about 2.96ped/s as the tunnel width expanded by 1m. This paper offers a novel method for reducing potential losses caused by crowd emergencies and can be a valuable reference for managing pedestrian flows and designing public places.
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R, Shaamili. "A Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd." Computational Intelligence and Machine Learning 3, no. 2 (October 14, 2022): 9–14. http://dx.doi.org/10.36647/ciml/03.02.a002.

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In populated cities, we often find crowded events like political meetings, religious festivals, music concerts, and events in shopping malls, which have more safety issues. Smart surveillance systems are used in big cities to keep crowds safe and make crowd security less complicated and more accurate. However, the surveillance systems proposed for a crowd are monitored by human agents, which are inefficient, error-prone, and overwhelming. Even with deep learning-based feature engineering in crowds, many variants of crowd analysis still lack attention and are technically unaddressed. Considering this scenario, the smart system requires the most advanced techniques to monitor the security of the crowd. Crowd analysis is commonly divided into crowd statics and behavior analysis. This paper explores more about crowd behaviour analysis, pedestrian and group detection which describes the movements that are noticed in the crowd image. Subsequently, the issues of the current methodology of pedestrian detection, datasets, and evaluation criteria are analyzed. Keyword : Crowd Analysis, Pedestrian and group detection, deep learning, Crowd IoT analysis, Human Activity Recognition.
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Dissertations / Theses on the topic "Pedestrian crowds"

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Johansson, Anders. "Data-Driven Modeling of Pedestrian Crowds." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-20900.

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At the starting point of the work leading to this doctoral thesis, in January 2005, the work on pedestrians was almost exclusively oriented towards computer simulations and on evacuation experiments. Since then, there have been many studies on new methods for extracting empirical data of pedestrian movements (mainly based on video analysis, lasers, and infrared cameras), but most of the work is still focused on artificial setups for crowds moving through corridors and crowds passing bottlenecks. Even though these controlled experiments are important to understand crowd dynamics, there is a knowledge gap between these experiments and the understanding of the dynamics leading to and occurring during large crowd disasters, when sometimes hundreds of thousands or even millions of pedestrians are involved. Numerous crowd disasters occur every year at large gatherings around the world. Unfortunately, the information about the (spatio-temporal) development of these events tend to be qualitative rather than quantitative. Video recordings from the crowd disaster in Mina, Kingdom of Saudi Arabia, on the 12th of January 2006, where hundreds of pilgrims lost their lives during the annual Muslim pilgrimage to Makkah, gave the possibility to scientifically evaluate the dynamics of the crowd. With this video material, it was possible to observe and analyze the behavior of the crowd under increasing crowd density, leading to the disaster. Based on the insights from the analysis of the crowd disaster described above, new tools and measures to detect and avoid critical crowd conditions have been proposed, and some of them have been implemented in order to reduce the likelihood of similar disasters in the future. Further contributions of this thesis are to empirically evaluate many of the previous assumptions used for pedestrian simulations. These assumptions are: * A pedestrian avoids collisions by changing her or his walking speed by an acceleration which is exponentially decaying with the distance to the pedestrian or object being avoided. * A pedestrian reacts stronger to what happens in front of her or him, than to what happens behind the back. * The movement of a crowd of pedestrians always follows a smooth flow-density relationship, called the fundamental diagram. * The walking speed will settle at 0 m/s at a specific maximum crowd density. The first two assumptions were found to be consistent with the data, but the pedestrian-flow theory had to be revised, since the two latter assumptions do not always hold. When these fundamental parts of pedestrian motion and avoiding maneuvers had been investigated, an improved version of the social-force-model was formulated. In order to enable the revision of previous works and the analysis of the crowd disaster mentioned above, algorithms used for video-tracking have been introduced. The novelty of this work concerns the uniqueness and quantity of data on which the algorithms are validated and calibrated, but also the focus on analyzing millions of pedestrians rather than hundreds. The aim of this thesis is to move from theoretical models and controlled lab conditions to applicable models for real-world conditions
Als diese Dissertation im Januar 2005 begonnen wurde, nutzten wissenschaftliche Untersuchungen von Fußgängern fast ausschließlich Computersimulationen und Evakuierungsexperimente. Seit dem haben viele Wissenschaftler an einer Verbesserung der Methoden gearbeitet. Heute werden empirische Daten mit Hilfe von Videoanalysen, Laser- und Infrarotkameras erhoben.Jedoch konzentrieren sich viele dieser Arbeiten auf künstliche Setups, in denen sich Fußgängermassen durch Korridore und Engpässe bewegen. Diese Experimente erlauben es, Massenbewegungen zu verstehen. Jedoch gibt es immer noch Forschungslücken. Es ist schwierig, unter solch kontrollierten Bedingungen Fortschritte darin zu erzielen, die auftretenden Dyamiken bei großen Katastrophen zu verstehen, in denen manchmal Hunderttausende oder sogar Millionen von Fußgängern involviert sind. Immer wieder kommt es zu Katastrophen in großen Menschenmengen. Leider sind von diesen Ereignissen häufig nur qualitative Informationen anstelle von quantitativen Daten erhältlich. Es ergab sich die besondere Gelegenheit, quantitatives Filmmaterial über eine Katastrophe in Mina (Königreich Saudi--Arabien) zu erhalten. Dort starben am 12. Januar 2006 hunderte von Pilgern während der jährlichen muslimischen Pilgerfahrt nach Mekka. Mit dem erhobenen Videomaterial konnte nachvollzogen werden, wie die Menschenmenge zuerst unbehindert fließen konnte, dann immer dichter wurde und wie es schließlich zur Katastrophe kam. Von den Erkenntnissen der Analyse der oben beschriebenen Katastrophe konnten neue Methoden entwickelt werden, die dabei helfen können, ähnliche Katastrophen in Zukunft zu vermeiden. Ein weiterer Beitrag dieser Dissertation besteht darin, einige Annahmen, die üblicherweise bei der Simulation von Fußgängerdynamiken gemacht werden, in Frage zu stellen und zu überarbeiten. Diese Annahmen sind: (1) Ein Fußgänger verhindert Zusammenstöße, indem er seine Schrittgeschwindigkeit so verändert, dass seine Beschleunigung exponentiell mit der Distanz zu dem zu umgehenden Fußgänger oder Objekt abnimmt. (2) Ein Fußgänger zeigt stärkere Reaktionen auf Ereignisse, die vor ihm passieren, als auf Ereignisse, die hinter ihm passieren. (3) Die Bewegung eines in einer Menschenmenge befindlichen Fußgängers folgt immer dem Strömungs--Dichte Verhältnis, was als Fundamental-Diagramm bezeichnet wird. (4) Die Laufgeschwindigkeit eines Fußgängers erreicht bei maximaler Menschendichte einem Wert von 0 m/s. Die ersten beiden Annahmen wurden von den empirischen Daten bestätigt. Unsere Analysen zeigen jedoch, dass die Annahmen 3 und 4 nicht immer gültig sind. Somit müssen Standardtheorien von Fußgängerdynamiken überarbeitet werden. Im Anschluß an die Analyse dieser fundamentalen Aspekte von Fußgängerverhalten und dem Verhalten bei Ausweichmanövern wird das Social-Force-Modell weiterentwickelt. Um auf vorhergehenden Arbeiten aufzubauen und um die oben beschriebene Katastrophe analysieren zu können, werden Algorithmen für die Video-Verfolgung von Fußgängerbewegungen entwickelt. Das Neue bei diesem Teil der Arbeit liegt nicht nur in dem verwendeten Verfahren selbst, sondern auch in der Einzigartigkeit und der großen Menge an verwendeten Daten, die mit diesem Verfahren analysiert werden. Ein zentrales Ziel dieser Arbeit besteht demnach in einer wissenschaftlichen Weiterentwicklung von theoretischen Modellen und kontrollierten Laborexperimenten hin zu Modellen, die unter realen Bedingungen tatsächlich anwendbar sind. Die Analyse von Fußgängern ist ein interdisziplinäres Feld, das von verschiedenen wissenschaftlichen Disziplinen mit verschiedenen Zielen betrieben wird. Leider gab es bislang wenig Bemühungen, die Resultate innerhalb dieser Teilgebiete im Rahmen einer konsistenten Theorie zu vereinen. Als seltene Ausnahmen können die Arbeiten von Teknomo und Antonini genannt werden. Diese Dissertation verfolgt das Ziel, diese theoretische Vereinigung weiter voran zu treiben. Dazu muss man zwischen der Neuerfindung des Rades und der Wiederverwendung nicht geprüfter Resultate abwägen. Dementsprechend ist ein Teil dieser Dissertation dem Vorhaben gewidmet, bisherige Forschung im Lichte empirischer Daten und neuer Methoden zu evaluieren. Da sich die Arbeit mit recht unterschiedlichen Aspekten von Fußgängerverhalten beschäftigt, konzentriert sich die Analyse in verschiedenen Teilen der Arbeit auf einige ausgewählte, alternative Modelle. Insbesondere bei der Modellierung und Simulation wird anstelle einer eingehenden Übersicht verschiedener Modelle eine Diskussion des speziellen Social-Force Modells präsentiert
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Vandoni, Jennifer. "Ensemble Methods for Pedestrian Detection in Dense Crowds." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS116/document.

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Cette thèse s’intéresse à la détection des piétons dans des foules très denses depuis un système mono-camera, avec comme but d’obtenir des détections localisées de toutes les personnes. Ces détections peuvent être utilisées soit pour obtenir une estimation robuste de la densité, soit pour initialiser un algorithme de suivi. Les méthodologies classiques utilisées pour la détection de piétons s’adaptent mal au cas où seulement les têtes sont visibles, de part l’absence d’arrière-plan, l’homogénéité visuelle de la foule, la petite taille des objets et la présence d’occultations très fortes. En présence de problèmes difficiles tels que notre application, les approches à base d’apprentissage supervisé sont bien adaptées. Nous considérons un système à plusieurs classifieurs (Multiple Classifier System, MCS), composé de deux ensembles différents, le premier basé sur les classifieurs SVM (SVM- ensemble) et le deuxième basé sur les CNN (CNN-ensemble), combinés dans le cadre de la Théorie des Fonctions de Croyance (TFC). L’ensemble SVM est composé de plusieurs SVM exploitant les données issues d’un descripteur différent. La TFC nous permet de prendre en compte une valeur d’imprécision supposée correspondre soit à une imprécision dans la procédure de calibration, soit à une imprécision spatiale. Cependant, le manque de données labellisées pour le cas des foules très denses nuit à la génération d’ensembles de données d’entrainement et de validation robustes. Nous avons proposé un algorithme d’apprentissage actif de type Query-by- Committee (QBC) qui permet de sélectionner automatiquement de nouveaux échantillons d’apprentissage. Cet algorithme s’appuie sur des mesures évidentielles déduites des fonctions de croyance. Pour le second ensemble, pour exploiter les avancées de l’apprentissage profond, nous avons reformulé notre problème comme une tâche de segmentation en soft labels. Une architecture entièrement convolutionelle a été conçue pour détecter les petits objets grâce à des convolutions dilatées. Nous nous sommes appuyés sur la technique du dropout pour obtenir un ensemble CNN capable d’évaluer la fiabilité sur les prédictions du réseau lors de l’inférence. Les réalisations de cet ensemble sont ensuite combinées dans le cadre de la TFC. Pour conclure, nous montrons que la sortie du MCS peut être utile aussi pour le comptage de personnes. Nous avons proposé une méthodologie d’évaluation multi-échelle, très utile pour la communauté de modélisation car elle lie incertitude (probabilité d’erreur) et imprécision sur les valeurs de densité estimées
This study deals with pedestrian detection in high- density crowds from a mono-camera system. The detections can be then used both to obtain robust density estimation, and to initialize a tracking algorithm. One of the most difficult challenges is that usual pedestrian detection methodologies do not scale well to high-density crowds, for reasons such as absence of background, high visual homogeneity, small size of the objects, and heavy occlusions. We cast the detection problem as a Multiple Classifier System (MCS), composed by two different ensembles of classifiers, the first one based on SVM (SVM-ensemble) and the second one based on CNN (CNN-ensemble), combined relying on the Belief Function Theory (BFT) to exploit their strengths for pixel-wise classification. SVM-ensemble is composed by several SVM detectors based on different gradient, texture and orientation descriptors, able to tackle the problem from different perspectives. BFT allows us to take into account the imprecision in addition to the uncertainty value provided by each classifier, which we consider coming from possible errors in the calibration procedure and from pixel neighbor's heterogeneity in the image space. However, scarcity of labeled data for specific dense crowd contexts reflects in the impossibility to obtain robust training and validation sets. By exploiting belief functions directly derived from the classifiers' combination, we propose an evidential Query-by-Committee (QBC) active learning algorithm to automatically select the most informative training samples. On the other side, we explore deep learning techniques by casting the problem as a segmentation task with soft labels, with a fully convolutional network designed to recover small objects thanks to a tailored use of dilated convolutions. In order to obtain a pixel-wise measure of reliability about the network's predictions, we create a CNN- ensemble by means of dropout at inference time, and we combine the different obtained realizations in the context of BFT. Finally, we show that the output map given by the MCS can be employed to perform people counting. We propose an evaluation method that can be applied at every scale, providing also uncertainty bounds on the estimated density
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Berton, Florian. "Immersive virtual crowds : evaluation of pedestrian behaviours in virtual reality." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S056.

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La réalité virtuelle (RV) est devenu un outil de plus en plus utilisé afin d'étudier le comportement humain. En effet, son utilisation permet d'avoir un contrôle absolu sur les conditions expérimentales et de reproduire le même stimulus pour tous les participants. Dans cette thèse, nous utilisons la RV pour étudier le comportement piétons dans les foules afin par la suite d'améliorer les simulateurs de foules. En particulier nous nous intéressons à l'analyse couplée de la marche et du regard pour pouvoir comprendre et modéliser le voisinage d'interaction lors de la navigation. Dans nos premiers travaux, nous nous avons évalué l'impact de la RV sur l'activité du regard lors d’une interaction entre deux piétons, dans une étude où les participants réalisaient une tâche d'évitement de collision dans un environnement réel et virtuel. Par la suite nous nous sommes intéressés à une situation plus complexe qui est la navigation dans une rue peuplée. Nous avons de nouveau évalué l'impact de la RV sur l'activité du regard, puis nous nous sommes intéressé à l'impact de la densité de la foule sur cette activité. Finalement, dans une troisième étude nous avons simulé, en utilisant un rendu haptique, les collisions se produisant lors de la navigation dans une foule dense, et nous avons évalué l'influence de tel rendu sur la navigation des participants. En conclusion, nos résultats montrent que la réalité virtuelle est un outil pertinent pour l'étude du comportement des piétons dans les foules. En particulier, avec les récentes innovations technologiques, cet outil est adapté à l'étude de l'activité du regard, qui a d’ailleurs été peu explorée jusqu'à présent pour ce type de situation
Virtual Reality (VR) has become more and more used as a tool to study human behaviour. Indeed, its use provides absolute control over experimental conditions and can reproduce the same stimulus for all participants. In this thesis, we use VR to investigate pedestrian behaviour in crowds in order to subsequently improve crowd simulators. In particular we are interested in a coupled analysis of locomotion and gaze in order to understand and model the interaction neighbourhood during navigation. In our first work, we evaluated the impact of VR on gaze activity during an interaction between two pedestrians, in a study where participants performed a collision avoidance task in a real and virtual environment. We then studied a more complex situation which is the navigation in a crowded street. We again evaluated the impact of VR on gaze activity and then explored the impact of crowd density on this activity. Finally, in a third study we simulated the collisions that occur when navigating in a dense crowd using haptic rendering, and evaluated the influence of such rendering on participants' locomotion. In conclusion, our results show that VR is a relevant tool to study pedestrian behaviour in crowds. In particular, with recent technological innovations, this tool is appropriate for the study of gaze activity, which to date has been little explored for this kind of situation
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Feng, Weinan. "Multiple Human Body Detection in Crowds." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-12352.

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The objective of this project is to use digital imaging devices to monitor a delineated area of the public space and to register statistics about people moving across this area. A feasible detecting approach, which is based on background subtraction, has been developed and has been tested on 39 images. Individual pedestrians in images can be detected and counted. The approach is suitably used to detect and count pedestrians without overlapping. Accuracy rate of detection is higher than 80%.
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Makmul, Juntima [Verfasser], and Simone [Akademischer Betreuer] Göttlich. "Microscopic and macroscopic models for pedestrian crowds / Juntima Makmul. Betreuer: Simone Göttlich." Mannheim : Universitätsbibliothek Mannheim, 2016. http://d-nb.info/1099910633/34.

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Bain, Nicolas. "Hydrodynamics of polarized crowds : experiments and theory." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEN078/document.

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Modéliser le mouvement des foules humaines est essentiel pour des situations aussi diverses que la prévention de risque dans les lieux publics, la planification d’évènements ou la création d’animations visuelles réalistes. Cependant, la difficulté de mener des expériences quantitatives limite notre compréhension de la dynamique des piétons, et le manque de mesures de référence rend impossible une comparaison poussée des modèles existants. Cette thèse tente d’augmenter notre compréhension des foules humaines par deux approches distinctes. Dans un premier temps, nous avons conduit une étude numérique et théorique pour étudier formation de lignes au sein de flux bidirectionnels d'agents motiles. Nous avons montré qu’une transition de phase critique du second ordre séparait un état mélangé d’un état constitué de lignes géantes le long desquelles se déplacent les agents visants une même direction. Cette séparation est caractéristique des systèmes actifs. Une approche hydrodynamique nous a ensuite permis de prouver que les phases mélangées sont aussi algébriquement corrélées dans la direction longitudinale. Nous avons expliqué et montré que ces fortes corrélations sont génériques de tous systèmes de flux bidirectionnels, qu’ils soient constitués de particules forcées ou de particules actives. Dans un second temps, nous avons mené une campagne expérimentale de grande envergure afin d’établir une expérience de référence des foules humaines. Nous avons pour cela choisi un système modèle, la zone d’attente de marathons. Dans ces foules de dizaines de milliers d’individus, nous avons quantitativement établi que les fluctuations de vitesse se propagent sur de grandes échelles, alors que les variations d’orientation s’évanouissent en quelques secondes. Grâce à ces mesures, nous avons construit une théorie prédictive hydrodynamique des foules polarisées
Modelling crowd motion is central to situations as diverse as risk prevention in mass events and visual effects rendering in the motion picture industry. The difficulty to perform quantitative measurements in model experiments, and the lack of reference experimental system, have however strongly limited our ability to model and control pedestrian flows. The aim of this thesis is to strengthen our understanding of human crowds, following two distinct approaches.First, we designed a numerical model to study the lane formation process among bidirectional flows of motile particles. We first evidenced the existence of two distinct phases: one fully laned and one homogeneously mixed, separated by a critical phase transition, unique to active systems. We then showed with a hydrodynamic approach that the mixed phase is algebraically correlated in the direction of the flow. We elucidated the origin of these strong correlations and proved that they were a universal feature of any system of oppositely moving particles, active of passive.Second, we conducted a substantial experimental campaign to establish a model experiment of human crowds. For that purpose we performed systematic measurements on crowds composed of tens of thousands of road-race participants in start corrals, a geometrically simple setup. We established that speed information propagates through polarized crowds over system spanning scales, while orientational information is lost in a few seconds. Building on these observations, we laid out a hydrodynamic theory of polarized crowds and demonstrated its predictive power
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KHAN, SULTAN DAUD. "Automatic Detection and Computer Vision Analysis of Flow Dynamics and Social Groups in Pedestrian Crowds." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/102644.

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Computer vision played a vital role in the field of video surveillance. However, recent developed computer vision algorithms rarely solve the problems related to real time crowd management. The phenomena of crowd like sports, festivals, concerts, political gatherings etc, are mostly observed in urban areas, which attracts hundreds of thousands people. In this thesis, we have developed algorithms that overcome some of the challenges encountered in videos of crowded environments such as sporting events, religious festivals, parades, concerts, train stations, airports, and malls. The main theme of this thesis is two fold ,i.e, understanding crowd dynamics in videos of (i), high density crowds and (ii) low density crowds. Typical examples of high density crowds include marathons, religious festivals while malls, airports, subways etc covers low dense situations. In this thesis, we adopt different approaches in order to deal with different kinds of problems coming from these two categories of crowd. In particular, first part of the thesis, we adopt holistic approach to generate a global representation of the scene that captures both dynamics of the crowd and structure of the scene. This was achieved by extracting global features, i.e optical flow from the scene. For the crowd flow segmentation problem, the optical flows vectors are clustered by using K-means clustering followed by the blob absorption approach. Using the segmentation information, we continue to estimate the number of people in each segment by carrying out the blob analysis and blob size optimization approach. This approach however, provide useful information for understanding crowd dynamics yet it lacks significant information for understanding crowd behavior. Therefore, in this thesis, the current crowd flow segmentation and counting approach is further extended in order to coup the challenges of crowd behavior understanding. The extension adopts optical flow for the identification of pedestrian movements, and it considers the analyzed video as a set of sequences. The latter are analyzed separately, producing tracklets that are then clustered to produce global trajectories, defining both sources and sinks, but also characterizing the movement of pedestrians in the scene. In the second part of the thesis, We propose a novel approach for automatic detection of social groups of pedestrians in crowds by considering only start (source) and stop (sink) locations of pedestrian trajectories. We build an Association Matrix that captures the joint probability distribution of starts and stops locations of all pedestrian trajectories to all other pedestrian trajectories in the scene. Pedestrians exhibiting similar distribution are combining in a group, where as similarity among the distributions is measuread by KL Divergence We adopt bottom-up hierarchical clustering approach, which is three step processes. In first step, we treat all the individuals as independent clusters, In the second step, couples are detected and after pruning of bad couples, Adjacency matrix is generated. Later on, in step three, using the Adjacency Matrix, groups of couples, those have strong intergroup closeness (similarity) are merged into a larger group..
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Bisagno, Niccol&#242. "On simulating and predicting pedestrian trajectories in a crowd." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/256722.

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Crowds of people are gathering at multiple venues, such as concerts, political rallies, as well as in commercial malls, or just simply walking on the streets. More and more people are flocking to live in urban areas, thus generating a lot of scenarios of crowds. As a consequence, there is an increasing demand for automatic tools that can analyze and predict the behavior of crowds to ensure safety. Crowd motion analysis is a key feature in surveillance and monitoring applications, providing useful hints about potential threats to safety and security in urban and public spaces. It is well known that people gatherings are generally difficult to model, due to the diversity of the agents composing the crowd. Each individual is unique, being driven not only by the destination but also by personality traits and attitude. The domain of crowd analysis has been widely investigated in the literature. However, crowd gatherings have sometimes resulted in dangerous scenarios in recent years, such as stampedes or during dangerous situations. To take a step toward ensuring the safety of crowds, in this work we investigate two main research problems: we try to predict each person future position and we try to understand which are the key factors for simulating crowds. Predicting in advance how a mass of people will fare in a given space would help in ensuring the safety of public gatherings.
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Bisagno, Niccolò. "On simulating and predicting pedestrian trajectories in a crowd." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/256722.

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Crowds of people are gathering at multiple venues, such as concerts, political rallies, as well as in commercial malls, or just simply walking on the streets. More and more people are flocking to live in urban areas, thus generating a lot of scenarios of crowds. As a consequence, there is an increasing demand for automatic tools that can analyze and predict the behavior of crowds to ensure safety. Crowd motion analysis is a key feature in surveillance and monitoring applications, providing useful hints about potential threats to safety and security in urban and public spaces. It is well known that people gatherings are generally difficult to model, due to the diversity of the agents composing the crowd. Each individual is unique, being driven not only by the destination but also by personality traits and attitude. The domain of crowd analysis has been widely investigated in the literature. However, crowd gatherings have sometimes resulted in dangerous scenarios in recent years, such as stampedes or during dangerous situations. To take a step toward ensuring the safety of crowds, in this work we investigate two main research problems: we try to predict each person future position and we try to understand which are the key factors for simulating crowds. Predicting in advance how a mass of people will fare in a given space would help in ensuring the safety of public gatherings.
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Zäll, Emma. "Modelling Pedestrian-Induced Vertical Vibrations of Footbridges." Thesis, Umeå universitet, Institutionen för fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-101831.

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A pedestrian crowd walking on a footbridge causes the footbridge to vibrate. These vibrations become an issue of serviceability and can give rise to discomfort for the pedestrians, whereby they should, to as large extent as possible, be prevented. Currently, there is a lack of reliable models to describe a dynamic load on a footbridge, due to a walking crowd. Therefore, there is a need for such models. Lately, a great amount of research has been carried out on the subject pedestrian-induced vibrations of footbridges, though most of it with focus on lateral vibrations. Conversely, this project has been performed aiming to accurately model pedestrian-induced vertical vibrations of a general footbridge. For that purpose, starting from an existing model, a somewhat improved model, comprising three sub-model, has been developed. The sub-models are: one model of the pedestrian crowd walking along the footbridge, one model describing the load from the pedestrian footstep and one model describing the interaction between the pedestrians and the footbridge. In order to get statistically reliable results, numerous simulations of the pedestrian-induced vertical vibrations of a specific footbridge have been performed, using the developed model. Averaging the results over the simulations, we could conclude that the model gives an average error of 7 %, compared to experimental data. The measured quantity giving these results was the absolute maximum value of the acceleration at the midpoint of the footbridge. The achieved dynamical response of the footbridge is qualitatively satisfying, while the quantitative error is larger than we hoped for, whereby we conclude that further improvement of the model is needed before we are able to accurately model pedestrian-induced vertical vibrations of footbridges.
När en folksamling går över en gångbro uppstår vibrationer i gångbron. Dessa vibrationer påverkar brons användbarhet och kan ge upphov till obehagskänsla hos fotgängarna, vilket gör att vibrationerna i största möjliga utsträckning bör motverkas. I nuläget saknas pålitliga modeller för att beskriva den dynamiska last en gångbro utsätts för när en folksamling går över den. Således föreligger ett behov att utveckla en sådan modell. Under de senaste decennierna har mycket forskning utförts inom området människoinducerade vibrationer i gångbroar. Dock har merparten av denna forskning berört endast laterala vibrationer. Detta projekt däremot, har genomförts med syftet att, med ett noggrant resultat, modellera människoinducerade vertikala vibrationer i en generell gångbro. För att uppnå detta har jag utgått från en befintlig modell och från den utvecklat en ny modell bestående av tre delmodeller. De tre delmodellerna är: en modell som beskriver hur folksamlingen rör sig över gångbron, en modell som beskriver den kraft det mänskliga fotsteget uträttar på gångbron och en modell som beskriver interaktionen mellan fotgängarna och gångbron. För att uppnå statistiskt tillförlitliga resultat har modellen som utvecklats i detta projekt använts för att utföra åtskilliga simuleringar av människoinducerade vertikala vibrationer i en specifik gångbro. Om vi medelvärdesbildar resultaten över simuleringarna framgår det att modellen som utvecklats ger ett resultat som avviker med 7 % från experimentellt data. Detta gäller för den maximala accelerationen vid gångbrons mittpunkt. Den resulterande dynamiska responsen ser kvalitativt sett bra ut, medan den kvantitativa avvikelsen är större än vi hoppats på. Därför drar vi slutsatsen att vidare förbättringar av modellen behövs för att den ska kunna användas till att på ett noggrant sätt modellera människoinducerade vertikala vibrationer i gångbroar.
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Books on the topic "Pedestrian crowds"

1

Pushkin, Kachroo, ed. Pedestrian dynamics: Feedback control of crowd evacuation. Berlin: Springer, 2008.

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Peacock, Richard D. Pedestrian and Evacuation Dynamics. Boston, MA: Springer Science+Business Media, LLC, 2011.

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Pushkin, Kachroo, ed. Pedestrian dynamics: Feedback control of crowd evacuation. Berlin: Springer, 2008.

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Abd Rahman, Noorhazlinda. Crowd Behavior Simulation of Pedestrians During Evacuation Process. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1846-7.

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(Editor), Michael Schreckenberg, and Som Deo Sharma (Editor), eds. Pedestrian and Evacuation Dynamics. Springer, 2001.

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Sharma, Som Deo, and Michael Schreckenberg. Pedestrian and Evacuation Dynamics. Springer Berlin / Heidelberg, 2010.

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Weidmann, Ulrich, Michael Schreckenberg, and Uwe Kirsch. Pedestrian and Evacuation Dynamics 2012. Springer, 2016.

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Weidmann, Ulrich, Michael Schreckenberg, and Uwe Kirsch. Pedestrian and Evacuation Dynamics 2012. Springer London, Limited, 2014.

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Weidmann, Ulrich, Michael Schreckenberg, and Uwe Kirsch. Pedestrian and Evacuation Dynamics 2012. Springer, 2014.

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(Editor), Nathalie Waldau, Peter Gattermann (Editor), Hermann Knoflacher (Editor), and Michael Schreckenberg (Editor), eds. Pedestrian and Evacuation Dynamics 2005. Springer, 2007.

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Book chapters on the topic "Pedestrian crowds"

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Helbing, Dirk. "Self-organization in Pedestrian Crowds." In Understanding Complex Systems, 71–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24004-1_3.

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Nishinari, Katsuhiro, Yushi Suma, Daichi Yanagisawa, Akiyasu Tomoeda, Ayako Kimura, and Ryousuke Nishi. "Toward Smooth Movement of Crowds." In Pedestrian and Evacuation Dynamics 2008, 293–308. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04504-2_26.

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Korhonen, T., and S. Heliövaara. "FDS+Evac: Modelling Pedestrian Movement in Crowds." In Pedestrian and Evacuation Dynamics, 823–26. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-9725-8_81.

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Papelis, Y. E., L. J. Bair, S. Manepalli, P. Madhavan, R. Kady, and E. Weisel. "Modeling of Human Behavior in Crowds Using a Cognitive Feedback Approach." In Pedestrian and Evacuation Dynamics, 265–73. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-9725-8_24.

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Lyell, M., R. Flo, and M. Mejia-Tellez. "Simulation of Pedestrian Agent Crowds, with Crisis." In Unifying Themes in Complex Systems, 307–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-85081-6_39.

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Bärwolff, Günter, Minjie Chen, Frank Huth, Gregor Lämmel, Kai Nagel, Matthias Plaue, and Hartmut Schwandt. "Methods for Modeling and Simulation of Multi-destination Pedestrian Crowds." In Pedestrian and Evacuation Dynamics 2012, 775–88. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02447-9_65.

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Groner, N. E., E. Miller-Hooks, and L. Feng. "Prospects for the Design of Cognitive Systems that Manage the Movement of Large Crowds." In Pedestrian and Evacuation Dynamics, 725–33. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-9725-8_64.

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Creusot, Clement. "Local Segmentation for Pedestrian Tracking in Dense Crowds." In MultiMedia Modeling, 266–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04114-8_23.

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Reuter, Verena, Benjamin S. Bergner, Gerta Köster, Michael Seitz, Franz Treml, and Dirk Hartmann. "On Modeling Groups in Crowds: Empirical Evidence and Simulation Results Including Large Groups." In Pedestrian and Evacuation Dynamics 2012, 835–45. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02447-9_70.

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Chraibi, Mohcine, Andreas Schadschneider, and Armin Seyfried. "On Force-Based Modeling of Pedestrian Dynamics." In Modeling, Simulation and Visual Analysis of Crowds, 23–41. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8483-7_2.

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Conference papers on the topic "Pedestrian crowds"

1

Maruyama, Daichi, and Tatsuji Takahashi. "Density Avoidance in Pedestrian Crowds." In 8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ACM, 2015. http://dx.doi.org/10.4108/icst.bict.2014.258048.

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Gu, Qin, Chang Yun, and Zhigang Deng. "Perceiving motion transitions in pedestrian crowds." In the 17th ACM Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1889863.1889908.

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Kazama, Takenori, Chihiro Morishima, Takashi Nishitsuji, and Takuya Asaka. "Delay Tolerant Networks Considering Pedestrian Crowds." In 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW). IEEE, 2019. http://dx.doi.org/10.1109/candarw.2019.00016.

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"crowds&ped 2009: International Workshop on Crowds and Pedestrian Behavior." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.416.

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Giunchi, Daniele, Riccardo Bovo, Panayiotis Charalambous, Fotis Liarokapis, Alastair Shipman, Stuart James, Anthony Steed, and Thomas Heinis. "Perceived Realism of Pedestrian Crowds Trajectories in VR." In VRST '21: 27th ACM Symposium on Virtual Reality Software and Technology. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3489849.3489860.

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Favaretto, Rodolfo Migon, and Soraia Raupp Musse. "Emotion, Personality and Cultural Aspects in Crowds: towards a Geometrical Mind." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8308.

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In this work we proposed a computational model to extract pedestrian characteristics from video sequences. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. As one of the main contributions, four large dimensions of geometric characteristics (Big4GD) were proposed: I - Physical, II - Personal and Emotional, III - Social and IV - Cultural, which seek to describe the behavior of pedestrians and groups in the crowd. The GeoMind tool was developed for the purpose of detecting the four geometric dimensions from video sequences. In addition, several analyzes were carried out with the purpose of validating the proposed model, from comparing results with the literature, including the comparison of spontaneous multitudes from several countries and controlled experiments involving Fundamental Diagrams.
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Shang, Chong, Haizhou Ai, Zijie Zhuang, and Long Chen Rui Chen. "Improving Pedestrian Detection in Crowds With Synthetic Occlusion Images." In 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2018. http://dx.doi.org/10.1109/icmew.2018.8551575.

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De Luca, Antonio, Scott Lomax, and Marguerite Jeansonne Pinto. "Advanced analysis of a pedestrian bridge and considerations on crowd-structure interaction." In IABSE Symposium, Prague 2022: Challenges for Existing and Oncoming Structures. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2022. http://dx.doi.org/10.2749/prague.2022.1427.

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<p>Pedestrian bridges, grandstands, and other long-span structures may be subjected to crowd loading. Crowds have the dual effect to produce large forces and alter the structure’s modal properties and damping. Vibration testing of full-scale structures allows the verification of the modelling assumptions and design criteria.</p><p>This paper discusses the case study of a pedestrian bridge structure subjected to crowd loading. The results of the design finite element model of the bridge and the bridge performance were validated through field testing. The theoretical effects of crowds were analysed and compared to the experimental test data.</p>
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Vandoni, Jennifer, Emanuel Aldea, and Sylvie Le Hegarat-Mascle. "An evidential framework for pedestrian detection in high-density crowds." In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2017. http://dx.doi.org/10.1109/avss.2017.8078498.

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Krausz, Barbara, and Christian Bauckhage. "Analyzing pedestrian behavior in crowds for automatic detection of congestions." In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, 2011. http://dx.doi.org/10.1109/iccvw.2011.6130236.

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Reports on the topic "Pedestrian crowds"

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Kaminka, Gal. The Impact of Cultural Differences on Crowd Dynamics in Pedestrian and Evacuation Domains. Fort Belvoir, VA: Defense Technical Information Center, November 2011. http://dx.doi.org/10.21236/ada552369.

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Donoghue, Orna, and Rose Anne Kenny. AMBER CROSS CODE: Walking speed in middle-aged and older Irish adults and the implications for pedestrian traffic signals. The Irish Longitudinal Study on Ageing, November 2015. http://dx.doi.org/10.38018/tildare.2015-01.

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Kodupuganti, Swapneel R., Sonu Mathew, and Srinivas S. Pulugurtha. Modeling Operational Performance of Urban Roads with Heterogeneous Traffic Conditions. Mineta Transportation Institute, January 2021. http://dx.doi.org/10.31979/mti.2021.1802.

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The rapid growth in population and related demand for travel during the past few decades has had a catalytic effect on traffic congestion, air quality, and safety in many urban areas. Transportation managers and planners have planned for new facilities to cater to the needs of users of alternative modes of transportation (e.g., public transportation, walking, and bicycling) over the next decade. However, there are no widely accepted methods, nor there is enough evidence to justify whether such plans are instrumental in improving mobility of the transportation system. Therefore, this project researches the operational performance of urban roads with heterogeneous traffic conditions to improve the mobility and reliability of people and goods. A 4-mile stretch of the Blue Line light rail transit (LRT) extension, which connects Old Concord Rd and the University of North Carolina at Charlotte’s main campus on N Tryon St in Charlotte, North Carolina, was considered for travel time reliability analysis. The influence of crosswalks, sidewalks, trails, greenways, on-street bicycle lanes, bus/LRT routes and stops/stations, and street network characteristics on travel time reliability were comprehensively considered from a multimodal perspective. Likewise, a 2.5-mile-long section of the Blue Line LRT extension, which connects University City Blvd and Mallard Creek Church Rd on N Tryon St in Charlotte, North Carolina, was considered for simulation-based operational analysis. Vissim traffic simulation software was used to compute and compare delay, queue length, and maximum queue length at nine intersections to evaluate the influence of vehicles, LRT, pedestrians, and bicyclists, individually and/or combined. The statistical significance of variations in travel time reliability were particularly less in the case of links on N Tryon St with the Blue Line LRT extension. However, a decrease in travel time reliability on some links was observed on the parallel route (I-85) and cross-streets. While a decrease in vehicle delay on northbound and southbound approaches of N Tryon St was observed in most cases after the LRT is in operation, the cross-streets of N Tryon St incurred a relatively higher increase in delay after the LRT is in operation. The current pedestrian and bicycling activity levels seemed insignificant to have an influence on vehicle delay at intersections. The methodological approaches from this research can be used to assess the performance of a transportation facility and identify remedial solutions from a multimodal perspective.
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Douglas, Gordon, and David Moore. Analyzing the Use and Impacts of Oakland Slow Streets and Potential Scalability Beyond Covid-19. Mineta Transportation Institute, July 2022. http://dx.doi.org/10.31979/mti.2021.2152.

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This report presents the results of a mixed-methods study of the 2020-2022 Oakland Slow Streets program. An official response to the Covid-19 pandemic, the program used signs and temporary barricades to limit thru-traffic on 21 miles of city streets to create more and safer space for walking, cycling, and outdoor recreation. Researchers collected data throughout the summer of 2021 on seven designated slow streets plus one cross street and one control street for each – a total of 21 street segments representing conditions in seven different neighborhoods across Oakland. Data collection comprised in-person passerby counts, observations and photographs of local conditions, and logged traffic speed data. Findings vary widely across study sites. In certain cases, observed slow streets saw less car traffic or more bicycle/pedestrian use than one or both of their comparison streets, and in at least one case the slow street was clearly embraced by the local community and used as planners intended; in others the slow street was no different than neighboring streets. The study draws on these findings to identify local conditions that seem likely to make slow treet treatments more or less successful. However, acknowledging that all neighborhoods deserve safer streets and greater outdoor recreational opportunities, the authors argue that better community outreach must be implemented to ensure areas not predisposed to make full use of slow streets can have the opportunity to do so. The study also makes suggestions regarding the potential for rapid, low-cost bike and pedestrian street safety improvements going forward.
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Douglas, Gordon, and David Moore. Analyzing the Use and Impacts of Oakland Slow Streets and Potential Scalability Beyond Covid-19. Mineta Transportation Institute, July 2022. http://dx.doi.org/10.31979/mti.2022.2152.

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This report presents the results of a mixed-methods study of the 2020-2022 Oakland Slow Streets program. An official response to the Covid-19 pandemic, the program used signs and temporary barricades to limit thru-traffic on 21 miles of city streets to create more and safer space for walking, cycling, and outdoor recreation. Researchers collected data throughout the summer of 2021 on seven designated slow streets plus one cross street and one control street for each – a total of 21 street segments representing conditions in seven different neighborhoods across Oakland. Data collection comprised in-person passerby counts, observations and photographs of local conditions, and logged traffic speed data. Findings vary widely across study sites. In certain cases, observed slow streets saw less car traffic or more bicycle/pedestrian use than one or both of their comparison streets, and in at least one case the slow street was clearly embraced by the local community and used as planners intended; in others the slow street was no different than neighboring streets. The study draws on these findings to identify local conditions that seem likely to make slow treet treatments more or less successful. However, acknowledging that all neighborhoods deserve safer streets and greater outdoor recreational opportunities, the authors argue that better community outreach must be implemented to ensure areas not predisposed to make full use of slow streets can have the opportunity to do so. The study also makes suggestions regarding the potential for rapid, low-cost bike and pedestrian street safety improvements going forward.
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6

CAREC Road Safety Engineering Manual 4: Pedestrian Safety. Asian Development Bank, March 2021. http://dx.doi.org/10.22617/tim210073-2.

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This manual is a practical point of reference for the provision of safer pedestrian facilities in Central Asia Regional Economic Cooperation (CAREC) countries. It focuses on the physical road infrastructure that can help pedestrians safely cross, and walk along, roads. It also outlines proven facilities that have been shown to assist pedestrians including those in the high-risk groups. Aimed at engineers, project managers, planners, traffic police, and other decision-makers, the manual shows how wise investment in pedestrian facilities can save lives, prevent injuries, and return major economic benefits to CAREC countries.
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