Relevant bibliographies by topics / Crowd, Pedestrian, Proxemics, Simulation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Crowd, Pedestrian, Proxemics, Simulation'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Crowd, Pedestrian, Proxemics, Simulation"

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Qiu, Fasheng, and Xiaolin Hu. "Modeling group structures in pedestrian crowd simulation." Simulation Modelling Practice and Theory 18, no. 2 (2010): 190–205. http://dx.doi.org/10.1016/j.simpat.2009.10.005.

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Sarmady, Siamak, Fazilah Haron, and Abdullah Zawawi Talib. "Simulation of pedestrian movements using a fine grid cellular automata model." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1197. http://dx.doi.org/10.11591/ijai.v11.i4.pp1197-1212.

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Crowd simulation is used in evacuation and crowd safety inspections and in the study of the performance of crowd systems and animations. Cellular automata are extensively utilized in crowd modeling. In regular cellular automata models, each pedestrian occupies a single cell with the size of a pedestrian body. The movements of pedestrians resemble those of chess pieces on a chessboard because the space is divided into relatively large cells. Furthermore, all pedestrians feature the same body size and speed. This study proposes a fine grid cellular automata model that uses small cells and allows pedestrian bodies to occupy several cells. The model allows the use of different body sizes, shapes, and speeds for pedestrians. The model is also used to simulate the movements of pedestrians toward a specific target. A typical walkway scenario is considered to test and evaluate the proposed model. Pedestrian movements are smooth because of the fine grain discretization of movements, and simulation results match the empirical speed–density graphs with good accuracy.
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Li, Jun, and Haoxiang Zhang. "Crowd Evacuation Simulation Research Based on Improved Reciprocal Velocity Obstacles (RVO) Model with Path Planning and Emotion Contagion." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 3 (2021): 740–57. http://dx.doi.org/10.1177/03611981211056910.

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Crowd evacuation simulation is an important research topic for designing reasonable building layout and effective evacuation routes. The reciprocal velocity obstacles (RVO) model is a pedestrian motion model which is used, but it does not work when complex and multiple obstacles are present in the scene. This paper proposes an improved RVO model with path planning and emotion contagion for crowd evacuation simulation. The model uses the vertices of the obstacles to construct pedestrian path nodes for planning pedestrian evacuation paths. To make the pedestrian evacuation paths simulation results more reasonable, the safety and congestion of the path nodes are considered, to plan the shortest evacuation path. Finally, a contagious disease model is introduced to study the impact of emotion contagion on the evacuation process. A crowd evacuation simulation system is developed, and simulations have been carried out in a variety of scenarios. Experiments show that the model can effectively simulate crowd evacuation, providing a powerful reference for building and layout design.
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Vizzari, Giuseppe, and Thomas Cecconello. "Pedestrian Simulation with Reinforcement Learning: A Curriculum-Based Approach." Future Internet 15, no. 1 (2022): 12. http://dx.doi.org/10.3390/fi15010012.

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Pedestrian simulation is a consolidated but still lively area of research. State of the art models mostly take an agent-based perspective, in which pedestrian decisions are made according to a manually defined model. Reinforcement learning (RL), on the other hand, is used to train an agent situated in an environment how to act so as to maximize an accumulated numerical reward signal (a feedback provided by the environment to every chosen action). We explored the possibility of applying RL to pedestrian simulation. We carefully defined a reward function combining elements related to goal orientation, basic proxemics, and basic way-finding considerations. The proposed approach employs a particular training curriculum, a set of scenarios growing in difficulty supporting an incremental acquisition of general movement competences such as orientation, walking, and pedestrian interaction. The learned pedestrian behavioral model is applicable to situations not presented to the agents in the training phase, and seems therefore reasonably general. This paper describes the basic elements of the approach, the training procedure, and an experimentation within a software framework employing Unity and ML-Agents.
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Alrashed, Mohammed, and Jeff Shamma. "Agent Based Modelling and Simulation of Pedestrian Crowds in Panic Situations." Collective Dynamics 5 (August 12, 2020): A100. http://dx.doi.org/10.17815/cd.2020.100.

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The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics. Understanding the collective behaviors of panic stampedes is essential to reducing the risk of deadly crowd disasters. In this work, we use an agent-based formulation to model the collective human behavior in such crowd dynamics. We investigate the impact of panic behavior on crowd dynamics, as a specific form of collective behavior, by introducing a contagious panic parameter. The proposed model describes the intensity and spread of panic through the crowd. The corresponding panic parameter impacts each individual to represent a different variety of behaviors that can be associated with panic situations such as escaping danger, clustering, and pushing. Simulation results show contagious panic and pushing behavior, resulting in a more realistic crowd dynamics model.
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Jin, Lianghai, Mei Fang, Shu Chen, Wenfan Lei, and Yun Chen. "Tangential Change Behavior and Pedestrian Simulation of Multichannel Evacuation Crowd." Mathematical Problems in Engineering 2020 (October 21, 2020): 1–13. http://dx.doi.org/10.1155/2020/7649094.

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In evacuation, the velocity difference of adjacent pedestrians before and after often leads to tangential change of pedestrian location in channel. This tangential change behavior and its interaction disturb the stable state of crowd evacuation in multiple channels, which can affect the efficiency of crowd evacuation and even cause trampling accidents. This paper considers the dynamic comfort distance and the expected speed and analyzes the relative position changes after pedestrians change lanes. It investigates the conditions of tangential change behavior and defines the rules of tangential change behavior processing. Meanwhile, it investigates the crowd’s tangential change behavior and its interaction process, revealing the crowd evacuation mechanism of tangential change behavior conditions. Simulation results show that as the crowd density gradually increases, pedestrians exhibit the evolutionary characteristic of “no tangential change ⟶ occasional tangential change ⟶ frequent tangential change ⟶ closely following.” The evacuation speed is obviously influenced by pedestrian’s tangential change behavior and crowd density; when the pedestrian density ρ = 2.0 and ρ = 3.0 , the tangential change behavior not only makes the speed difference and fluctuation between different lanes great but also has the same effect on the average speed of pedestrians. The results of this study can provide theoretical insights into the organization of multichannel evacuation and expand the theoretical space of crowd dynamics in an evacuation.
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Muhammed, Danial A., Tarik A. Rashid, Abeer Alsadoon, et al. "An Improved Simulation Model for Pedestrian Crowd Evacuation." Mathematics 8, no. 12 (2020): 2171. http://dx.doi.org/10.3390/math8122171.

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This paper works on one of the most recent pedestrian crowd evacuation models—i.e., “a simulation model for pedestrian crowd evacuation based on various AI techniques”—which was developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it into the model. This method enables the developed model to find a more appropriate evacuation area design regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model’s output—i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location; then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs.
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Qiu, Fasheng, and Xiaolin Hu. "Spatial activity-based modeling for pedestrian crowd simulation." SIMULATION 89, no. 4 (2012): 451–65. http://dx.doi.org/10.1177/0037549711435950.

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Liu, Yuanyuan, and Toshiyuki Kaneda. "Using agent-based simulation for public space design based on the Shanghai Bund waterfront crowd disaster." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 2 (2020): 176–90. http://dx.doi.org/10.1017/s0890060420000049.

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AbstractWith growing city density and mass gatherings held all over the world in urban spaces, crowd disasters have been happening each year. In considering the avoidance of crowd disasters and the reduction of fatalities, it is important to analyze the efficient spatial layout of the public space in situations of high crowd density. Compared with traditional empirical design methods, computational approaches have better abilities for quantitative analysis and are gradually being adopted in the planning and management of the urban public space. In this paper, we investigated the official documents, publicly available videos, and materials of the Shanghai waterfront crowd disaster which happened on December 31, 2014. Based on the investigation, a detailed site survey was conducted and pedestrian flow data were acquired. To test the influence of different spatial layouts, an agent-based simulator is built, following the ASPFver4.0 (Agent Simulator of Pedestrian Flow) pedestrian walking rules. With the surveyed pedestrian flow data, the original spatial layout of the Shanghai Bund waterfront together with five other comparison scenarios are tested, including both space design and crowd management improvements. In the simulation results, the efficiencies of different space design and crowd management solutions are compared. The results show that even simple crowd control measures such as capacity reserve and more proper route planning will allow for a positive improvement in crowd safety. The results also compare the efficiency of different spatial operations and give general suggestions to the problems urban public space designers should consider in high-density environments.
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Usher, John M., Eric Kolstad, and Xuan Liu. "Simulation of Pedestrian Behavior in Intermodal Facilities." International Journal of Agent Technologies and Systems 2, no. 3 (2010): 66–82. http://dx.doi.org/10.4018/jats.2010070105.

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Planning pedestrian environments requires that designers understand how pedestrians interact with their environment and one another. With improved knowledge, the design and planning of pedestrian areas can provide improvements in safety, throughput, and utility. This paper provides an overview of the Intermodal Simulator for the Analysis of Pedestrian Traffic (ISAPT). It focuses on the methodologies used in simulation of the pedestrian traffic, including route planning and navigation. Several illustrations of the system’s ability to reproduce observed crowd behavior are provided.
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Dissertations / Theses on the topic "Crowd, Pedestrian, Proxemics, Simulation"

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GORRINI, ANDREA. "Empirical studies and computational results of a proxemic - based model of pedestrian crowd dynamics." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/50254.

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The thesis work is organized in two main parts. The first includes a review of the social science framework about crowd dynamics and proxemics, and the methodological approach. The second part consists of several empirical studies. A summary of the contents is provided as follows. Starting from the pioneering study of Gustave Le Bon (1897), the social science contributions about crowds are reviewed in Chapter 2 (Contagion-Transformation Theory, Elaborated Social Identity Model, Emergent Norm Theory, Affiliative Approach). Chapter 3 presents the proxemic theory, with reference to the notion of personal space and the group proxemic behavior in static and motion situations. Chapter 4 presents the methodological approach, as composed of: in vivo observation, in vitro experiments and in silico simulations. Chapter 5 proposed the results achieved by means of two observations performed at the Campus of the University of Milano-Bicocca (Italy) and the Vittorio Emanuele II gallery (Milan, Italy). Chapter 6 presents two experiments focused on the combined impact of turning path and grouping on pedestrian crowd dynamics and the size of pedestrian personal space. Chapter 7 presents a simulation campaign performed by using the platform MAKKSim. The results achieved have been compared with the collected empirical data for sake of model validation. The thesis ends with final remarks about the achieved results and future works towards the improvement of the computational model of MAKKSim.
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MANENTI, LORENZA ALESSANDRA. "Agent-based proxemic dynamics: crowd and groups simulation." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/42374.

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Considering the general overview on the Pedestrian Dynamics area, this thesis is focused in the area of pedestrian dynamics simulation, with the goal to study the phenomenon of groups as constitutive elements that compose a crowd, analyzing if their presence influences the dynamics of pedestrian flow and evaluating the impact of their contribution.
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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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Qiu, Fasheng. "A Framework for Group Modeling in Agent-Based Pedestrian Crowd Simulations." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/cs_diss/56.

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Pedestrian crowd simulation explores crowd behaviors in virtual environments. It is extensively studied in many areas, such as safety and civil engineering, transportation, social science, entertainment industry and so on. As a common phenomenon in pedestrian crowds, grouping can play important roles in crowd behaviors. To achieve more realistic simulations, it is important to support group modeling in crowd behaviors. Nevertheless, group modeling is still an open and challenging problem. The influence of groups on the dynamics of crowd movement has not been incorporated into most existing crowd models because of the complexity nature of social groups. This research develops a framework for group modeling in agent-based pedestrian crowd simulations. The framework includes multiple layers that support a systematic approach for modeling social groups in pedestrian crowd simulations. These layers include a simulation engine layer that provides efficient simulation engines to simulate the crowd model; a behavior-based agent modeling layers that supports developing agent models using the developed BehaviorSim simulation software; a group modeling layer that provides a well-defined way to model inter-group relationships and intra-group connections among pedestrian agents in a crowd; and finally a context modeling layer that allows users to incorporate various social and psychological models into the study of social groups in pedestrian crowd. Each layer utilizes the layer below it to fulfill its functionality, and together these layers provide an integrated framework for supporting group modeling in pedestrian crowd simulations. To our knowledge this work is the first one to focus on a systematic group modeling approach for pedestrian crowd simulations. This systematic modeling approach allows users to create social group simulation models in a well-defined way for studying the effect of social and psychological factors on crowd’s grouping behavior. To demonstrate the capability of the group modeling framework, we developed an application of dynamic grouping for pedestrian crowd simulations.
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Nishinari, Katsuhiro, Satoshi Kokubo, and Kazuhiro Yamamoto. "Simulation for pedestrian dynamics by real-coded cellular automata (RCA)." Elsevier, 2007. http://hdl.handle.net/2237/20045.

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Sorrentino, Luigi. "Simulation and optimization of crowd dynamics using a multiscale model." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/318.

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2010 - 2011<br>In the last decades, the modeling of crowd motion and pedestrian .ow has attracted the attention of applied mathematicians, because of an increasing num- ber of applications, in engineering and social sciences, dealing with this or similar complex systems, for design and optimization purposes. The crowd has caused many disasters, in the stadiums during some major sporting events as the "Hillsborough disaster" occurred on 15 April 1989 at Hills- borough, a football stadium, in She¢ eld, England, resulting in the deaths of 96 people, and 766 being injured that remains the deadliest stadium-related disaster in British history and one of the worst ever international football accidents. Other example is the "Heysel Stadium disaster" occurred on 29 May 1985 when escaping, fans were pressed against a wall in the Heysel Stadium in Brussels, Belgium, as a result of rioting before the start of the 1985 European Cup Final between Liv- erpool of England and Juventus of Italy. Thirty-nine Juventus fans died and 600 were injured. It is well know the case of the London Millennium Footbridge, that was closed the very day of its opening due to macroscopic lateral oscillations of the structure developing while pedestrians crossed the bridge. This phenomenon renewed the interest toward the investigation of these issues by means of mathe- matical modeling techniques. Other examples are emergency situations in crowded areas as airports or railway stations. In some cases, as the pedestrian disaster in Jamarat Bridge located in South Arabia, mathematical modeling and numerical simulation have already been successfully employed to study the dynamics of the .ow of pilgrims, so as to highlight critical circumstances under which crowd ac- cidents tend to occur and suggest counter-measures to improve the safety of the event. In the existing literature on mathematical modeling of human crowds we can distinguish two approaches: microscopic and macroscopic models. In model at microscopic scale pedestrians are described individually in their motion by ordinary di¤erential equations and problems are usually set in two-dimensional domains delimiting the walking area under consideration, with the presence of obstacles within the domain and a target. The basic modeling framework relies on classical Newtonian laws of point. The model at the macroscopic scale consists in using partial di¤erential equations, that is in describing the evolution in time and space of pedestrians supplemented by either suitable closure relations linking the velocity of the latter to their density or analogous balance law for the momentum. Again, typical guidelines in devising this kind of models are the concepts of preferred direction of motion and discomfort at high densities. In the framework of scalar conservation laws, a macroscopic onedimensional model has been proposed by Colombo and Rosini, resorting to some common ideas to vehicular tra¢ c modeling, with the speci.c aim of describing the transition from normal to panic conditions. Piccoli and Tosin propose to adopt a di¤erent macroscopic point of view, based on a measure-theoretical framework which has recently been introduced by Canuto et al. for coordination problems (rendez-vous) of multiagent systems. This approach consists in a discrete-time Eulerian macroscopic representation of the system via a family of measures which, pushed forward by some motion mappings, provide an estimate of the space occupancy by pedestrians at successive time steps. From the modeling point of view, this setting is particularly suitable to treat nonlocal interactions among pedestrians, obstacles, and wall boundary conditions. A microscopic approach is advantageous when one wants to model di¤erences among the individuals, random disturbances, or small environments. Moreover, it is the only reliable approach when one wants to track exactly the position of a few walkers. On the other hand, it may not be convenient to use a microscopic approach to model pedestrian .ow in large environments, due to the high com- putational e¤ort required. A macroscopic approach may be preferable to address optimization problems and analytical issues, as well as to handle experimental data. Nonetheless, despite the fact that self-organization phenomena are often visible only in large crowds, they are a consequence of strategical behaviors devel- oped by individual pedestrians. The two scales may reproduce the same features of the group behavior, thus providing a perfect matching between the results of the simulations for the micro- scopic and the macroscopic model in some test cases. This motivated the multiscale approach proposed by Cristiani, Piccoli and Tosin. Such an approach allows one to keep a macroscopic view without losing the right amount of .granularity,.which is crucial for the emergence of some self-organized patterns. Furthermore, the method allows one to introduce in a macroscopic (averaged) context some micro- scopic e¤ects, such as random disturbances or di¤erences among the individuals, in a fully justi.able manner from both the physical and the mathematical perspec- tive. In the model, microscopic and macroscopic scales coexist and continuously share information on the overall dynamics. More precisely, the microscopic part tracks the trajectories of single pedestrians and the macroscopic part the density of pedestrians using the same evolution equation duly interpreted in the sense of measures. In this respect, the two scales are indivisible. Starting from model of Cristiani, Piccoli and Tosin we have implemented algo- rithms to simulate the pedestrians motion toward a target to reach in a bounded area, with one or more obstacles inside. In this work di¤erent scenarios have been analyzed in order to .nd the obstacle con.guration which minimizes the pedes- trian average exit time. The optimization is achieved using to algorithms. The .rst one is based on the exhaustive exploration of all positions: the average exit time for all scenarios is computed and then the best one is chosen. The second algorithm is of steepest descent type according to which the obstacle con.guration corresponding to the minimum exit time is found using an iterative method. A variant has been introduced to the algorithm so to obtain a more e¢ cient proce- dure. The latter allows to .nd better solutions in few steps than other algorithms. Finally we performed other simulations with bounded domains like a classical .at with .ve rooms and two exits, comparing the results of three di¤erent scenario changing the positions of exit doors. [edited by author]<br>X n.s.
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Cabrero, Daniel Beatriz. "Automating crowd simulation: from parameter tuning to dynamic context-to-policy adaptation." Doctoral thesis, Universitat Pompeu Fabra, 2022. http://hdl.handle.net/10803/673251.

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Computer-generated crowds are becoming more and more frequent in films, video games and safety assessment applications. Many crowd simulation algorithms exist to address the needs of this diverse range of industries. Even though the underlying principles are similar, there are large differences between the resulting synthetic trajectories. Each algorithm has strengths and weaknesses that need to be weighted, and appropriate parameter values for them must be selected as well. These are not easy tasks and Machine Learning algorithms are often used to guide these decisions. In this work we study three of these tasks: parameter tuning, trajectory evaluation, and character policy selection and adaptation. Our results show the usefulness of the proposed methods to evaluate previously unseen synthetic trajectories to find appropriate parameter values for the algorithms without directly relying on real data. Moreover, by classifying the context of characters, we propose a policy adaptation strategy to improve crowd simulations.<br>Les multituds simulades per ordinador són cada cop més habituals en cinema, vídeo jocs i en aplicacions relacionades amb la seguretat. Existeixen molts algoritmes per simular multituds per adreçar tal varietat d’indústries. Tot i que els principis subjacents són similars, hi ha diferències entre les simulacions resultants. Cada algoritme té avantatges i inconvenients que s’han de valorar, i, a més a més, cal trobar valors pels seus paràmetres. Aquestes no són tasques senzilles i, sovint, es fan servir algoritmes d’aprenentatge automàtic per guiar aquestes decisions. Estudiem tres d’aquestes tasques: donar valor als paràmetres, avaluar trajectòries, i adaptar les polítiques. Els resultats demostren la utilitat dels mètodes proposats per avaluar trajectòries noves per tal de trobar valors apropiats pels paràmetres dels algorismes sense fer servir dades reals directament. A més a més, proposem una estratègia per adaptar la política de cada agent a través del reconeixement del context, millorant les simulacions.
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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<br>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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Kabalan, Bachar. "Dynamique des foules : modélisation du mouvement des piétons et forces associées engendrées." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1126/document.

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Que ce soit dans une rue commerçante, un supermarché ou un aéroport, les phénomènes de foule sont incontournables et nous affecte au quotidien. Elle constitue un système complexe dont la dynamique collective, résultant des interactions individuelles, est difficile à appréhender et a toujours intrigué les scientifiques de différents domaines. Grâce au progrès technologique, il est aujourd'hui possible de modéliser les mouvements de foule et de les reproduire en simulation. Les simulations de mouvement de foule permettent aux chercheurs de plusieurs disciplines, comme les sciences sociales ou la biomécanique, de mieux étudier et comprendre les mouvements des piétons et leurs interactions. Quant aux sciences de la sécurité et du transport, ils y voient des applications concrètes comme le développement de modèles de foule capables de simuler l'évacuation d'un lieu public de moyenne ou de forte affluence, afin que les futures constructions ou aménagements publics puissent offrir une qualité de sécurité et de service optimale pour les usagers. Dans le cadre de cette thèse, nous avons travaillé sur le perfectionnement du modèle discret proposé et développé par l'équipe dynamique du laboratoire Navier. Dans ce modèle, les actions et les décisions de chaque piéton sont traitées individuellement. Trois aspects du modèle ont été traités dans cette thèse. Le premier concerne la navigation des piétons vers leurs destinations. Dans notre modèle, un piéton est représenté par une particule ayant une direction et une allure souhaitées. Cette direction est obtenue par la résolution d'une équation eikonale. La solution de cette équation permet d'obtenir un champ de vitesses qui attribue à chaque piéton, en fonction de sa position, une direction vers sa destination. La résolution de l'équation une fois ou à une période quelconque donne la stratégie du chemin le plus court ou le plus rapide respectivement. Les effets des deux stratégies sur la dynamique collective de la foule sont comparés. Le deuxième consiste à gérer le comportement des piétons. Après avoir choisi son chemin, un piéton doit interagir avec l'environnement (obstacles, topologie, ...) et les autres piétons. Nous avons réussi à intégrer trois types de comportement dans notre modèle: (i) la poussée en utilisant une approche originale, basée sur la théorie des collisions des corps rigides dans un cadre thermodynamique rigoureux, (ii) le passage agressif (forcer son chemin) modélisé par une force sociale répulsive et (iii) l'évitement ``normal'' en adoptant une approche cognitive basée sur deux heuristiques. Les performances des trois méthodes ont été comparées pour plusieurs critères. Le dernier aspect concerne la validation et la vérification du modèle. Nous avons réalisé une étude de sensibilité et validé le modèle qualitativement et quantitativement. À l'aide d'un plan d'expérience numérique nous avons réussi à identifier les paramètres d'entrée ayant les effets principaux sur les résultats du modèle. De plus, nous avons trouvé les différentes interactions entre ces paramètres. En ce qui concerne la validation qualitative, nous avons réussi à reproduire plusieurs phénomènes d'auto-organisation. Enfin, nous avons testé la capacité de notre modèle à reproduire des résultats expérimentaux issus de la littérature. Nous avons choisi le cas du goulot d'étranglement. Les résultats du modèle et ceux de l'expérience ont été comparés. Ce modèle de foule a également été appliqué à l'acheminement des piétons dans la gare de Noisy-Champs. L'objectif de cette application est d'estimer le temps de stationnement des trains dans la gare<br>Crowds are present almost everywhere and affect several aspects of our lives. They are considered to be on of the most complex systems whose dynamics, resulting from individual interactions and giving rise to fascinating phenomena, is very difficult to understand and have always intrigued experts from various domains. The technological advancement, especially in computer performance, has allowed to model and simulate pedestrian movement. Research from different disciplines, such as social sciences and bio-mechanics, who are interested in studying crowd movement and pedestrian interactions were able to better examine and understand the dynamics of the crowd. Professionals from architects and transport planners to fire engineers and security advisors are also interested in crowd models that would help them to optimize the design and operation of a facility. In this thesis, we have worked on the imporvement of a discrete crowd model developed by the researchers from the dynamics group in Navier laboratory. In this model, the actions and decisions taken by each individual are treated. In its previous version, the model was used to simulate urgent evacuations. Three main aspects of the model were addressed in this thesis. The first one concerns pedestrian navigation towards a final destination. In our model, a pedestrian is represented by a disk having a willingness to head to a certain destination with a desired direction and a desired speed. A desired direction is attributed to each pedestrian, depending on his position from the exit, from a floor field that is obtained by solving the eikonal equation. Solving this equation a single time at the beginning of the simulation or several times at during the simulation allows us to obtain the shortest path or the fastest path strategy respectively. The influence of the two strategies on the collective dynamics of the crowds is compared. The second one consists of managing pedestrian-pedestrian interactions. After having chosen his/her direction according to one of the available strategies, a pedestrian is bound to interact with other pedestrians present on the chosen path. We have integrated three pedestrian behaviors in our model: (i) pushing by using an original approach based on the theory of rigid body collisions in a rigorous thermodynamics context, (ii) forcing one's way by introducing a social repulsive force and (iii) "normal" avoidance by using a cognitive approach based on two heuristics. The three methods are compared for different criteria. The last aspect is the validation and verification of the model. We have performed a sensibility study and validated the model qualitatively and quantitatively. Using a numerical experimental plan, we identified the input parameters that are the most statistically significant and estimated the effects of their interactions. Concerning qualitative validation, we showed that our model is able to reproduce several self-organization phenomena such as lane formation. Finally, our model was validated quantitatively for the case of a bottleneck. The experimental results are very close to the ones obtained from simulations. The model was also applied to pedestrian movement in the Noisy-Champs train station. The objective of the study was to estimate the train dwell time. The simulation results were similar to the observations
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Alrashed, Mohammed. "Control Theoretic Approaches to Computational Modeling and Risk Mitigation for Large Crowd Management." Diss., 2020. http://hdl.handle.net/10754/665965.

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We develop a computational framework for risk mitigation in high population density events. With increased global population, the frequency of high population density events is naturally increased. Therefore, risk-free crowd management plans are critical for efficient mobility, convenient daily life, resource management and most importantly mitigation of any inadvertent incidents and accidents such as stampedes. The status-quo for crowd management plans is the use of human experience/expert advice. However, most often such dependency on human experience is insufficient, flawed and results in inconvenience and tragic events. Motivated by these issues, we propose an agent-based mathematical model describing realistic human motion and simulating large dense crowds in a wide variety of events as a potential simulation testbed to trial crowd management plans. The developed model incorporates stylized mindset characteristics as an internal drive for physical behavior such as walking, running, and pushing. Furthermore, the model is combined with a visualisation of crowd movement. We develop analytic tools to quantify crowd dynamic features. The analytic tools will enable verification and validation to empirical evidence and surveillance video feed in both local and holistic representations of the crowd. This work addresses research problems in computational modeling of crowd dynamics, specifically: understanding and modeling the impact of a collective mindset on crowd dynamics versus mixtures of heterogeneous mindsets, the effect of social contagion of behaviors and decisions within the crowd, the competitive and aggressive pushing behaviors, and torso and steering dynamics.
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Book chapters on the topic "Crowd, Pedestrian, Proxemics, Simulation"

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Pettré, Julien, David Wolinski, and Anne-Hélène Olivier. "Velocity-Based Models for Crowd Simulation." In Pedestrian and Evacuation Dynamics 2012. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02447-9_88.

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Klüpfel, Hubert. "Models for Crowd Movement and Egress Simulation." In Pedestrian and Evacuation Dynamics 2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04504-2_65.

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Renc, Paweł, Maciej Bielech, Tomasz Pęcak, et al. "HPC Large-Scale Pedestrian Simulation Based on Proxemics Rules." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43222-5_43.

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Paris, Sébastien, Delphine Lefebvre, and Stéphane Donikian. "SIMULEM: Introducing Goal Oriented Behaviours in Crowd Simulation." In Pedestrian and Evacuation Dynamics 2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04504-2_40.

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Münchow, Stefan, Ia Enukidze, Stefan Sarstedt, and Thomas Thiel-Clemen. "WALK: A Modular Testbed for Crowd Evacuation Simulation." In Pedestrian and Evacuation Dynamics 2012. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02447-9_118.

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Peng, Gao, and Xu Ruihua. "3-Tier Architecture for Pedestrian Agent in Crowd Simulation." In Pedestrian and Evacuation Dynamics 2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04504-2_53.

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Seer, Stefan, Norbert Brändle, and Dietmar Bauer. "Design of Decision Rules for Crowd Controlling Using Macroscopic Pedestrian Flow Simulation." In Pedestrian and Evacuation Dynamics 2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04504-2_52.

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Cui, Q., M. Ichikawa, T. Kaneda, and H. Deguchi. "A Dynamic Simulation on Crowd Congestion in Large-Scale Terminal Station Complex in an Official Announcement Advisory Information." In Pedestrian and Evacuation Dynamics. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-9725-8_34.

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Zhou, Yayun, Wolfram Klein, and Hermann Georg Mayer. "Automatic Validation for Crowd Simulation: Test Suite for a Pedestrian Simulator Based on Different Scenarios." In Communications in Computer and Information Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27753-0_8.

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Kaneda, Toshiyuki, and Yanfeng He. "Modeling and Development of an Autonomous Pedestrian Agent — As a Simulation Tool for Crowd Analysis for Spatial Design." In Agent-Based Social Systems. Springer Japan, 2009. http://dx.doi.org/10.1007/978-4-431-87435-5_9.

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Conference papers on the topic "Crowd, Pedestrian, Proxemics, Simulation"

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Li, Tianlin, Yiping Yao, Wenjie Tang, and Feng Yao. "A Hybrid Pedestrian Motion Modeling Approach for Crowd Simulation." In 2017 10th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2017. http://dx.doi.org/10.1109/iscid.2017.133.

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Li, Shiwei, and Huimin Niu. "Simulation of Pedestrian Evacuation Flow Based on Crowd Space." In Fifth International Conference on Transportation Engineering. American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784479384.192.

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Butenuth, Matthias, Florian Burkert, Florian Schmidt, et al. "Integrating pedestrian simulation, tracking and event detection for crowd analysis." In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, 2011. http://dx.doi.org/10.1109/iccvw.2011.6130237.

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Zhou, Yayun, Wolfram Klein, and Hermann Georg Mayer. "Guideline for Crowd Evacuation Simulation - Validation of a Pedestrian Simulator with RiMEA Test Scenarios." In 4th International Conference on Smart Cities and Green ICT Systems. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005436700350042.

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Rimboux, Antoine, Rob Dupre, Eldriona Daci, et al. "Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation." In 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2019. http://dx.doi.org/10.1109/dcoss.2019.00070.

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Rojas, Francisco Arturo, and Hyun Seung Yang. "Immersive human-in-the-loop HMD evaluation of dynamic group behavior in a pedestrian crowd simulation that uses group agent-based steering." In the 12th ACM SIGGRAPH International Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2534329.2534336.

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Liang, Jing, Utsav Patel, Adarsh Jagan Sathyamoorthy, and Dinesh Manocha. "Crowd-Steer: Realtime Smooth and Collision-Free Robot Navigation in Densely Crowded Scenarios Trained using High-Fidelity Simulation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/583.

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We present a novel high fidelity 3-D simulator that significantly reduces the sim-to-real gap for collision avoidance in dense crowds using Deep Reinforcement Learning (DRL). Our simulator models realistic crowd and pedestrian behaviors, along with friction, sensor noise and delays in the simulated robot model. We also describe a technique to incrementally control the randomness and complexity of training scenarios to achieve better convergence and generalization capabilities. We demonstrate the effectiveness of our simulator by training a policy that fuses data from multiple perception sensors such as a 2-D lidar and a depth camera to detect pedestrians and computes smooth, collision-free velocities. Our novel reward function and multi-sensor formulation results in smooth and unobtrusive navigation. We have evaluated the learned policy on two differential drive robots and evaluate its performance in new dense crowd scenarios, narrow corridors, T and L-junctions, etc. We observe that our algorithm outperforms prior dynamic navigation techniques in terms of metrics such as success rate, trajectory length, mean time to goal, and smoothness.
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Stuart, Daniel, Keith Christensen, Anthony Chen, Ke-Cai Cao, Caibin Zeng, and YangQuan Chen. "A Framework for Modeling and Managing Mass Pedestrian Evacuations Involving Individuals With Disabilities: Networked Segways as Mobile Sensors and Actuators." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12652.

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Based on our previous work on Mobile Actuator and Sensor Network, Applied Fractional Calculus, Sensor Networks and BUMMPEE (Bottom-Up Modeling of Mass Pedestrian flows implications for the Effective Egress of individuals with disabilities), a general framework is proposed for modeling and managing Mass Pedestrian Evacuations (MPE) in this paper. A distinctive feature compared with previous work is the incorporation of Individuals with Disabilities (IwDs) in understanding modeling and control of mass pedestrians evacuations. Networked Segway Supported Responders (NSSR) have been firstly employed in the research of modeling and control/managing problem of crowd pedestrians as mobile sensors and mobile actuators. Future simulation and experimental results will be referenced for public policy professionals and planners for better evacuation policy making and route planning.
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