Pedestrian Dynamics: Modeling and Analyzing Cognitive Processes and Traffic Flows to Evaluate Facility Service Level

Walking is the oldest and foremost mode of transportation through history and the prevalence of walking has increased. Effective pedestrian model is crucial to evaluate pedestrian facility service level and to enhance pedestrian safety, performance, and satisfaction. The objectives of this study were to: (1) validate the efficacy of utilizing queueing network model, which predicts cognitive information processing time and task performance; (2) develop a generalized queueing network based cognitive information processing model that can be utilized and applied to construct pedestrian cognitive structure and estimate the reaction time with the first moment of service time distribution; (3) investigate pedestrian behavior through naturalistic and experimental observations to analyze the effects of environment settings and psychological factors in pedestrians; and (4) develop pedestrian level of service (LOS) metrics that are quick and practical to identify improvement points in pedestrian facility design. Two empirical and two analytical studies were conducted to address the research objectives. The first study investigated the efficacy of utilizing queueing network in modeling and predicting the cognitive information processing time. Motion capture system was utilized to collect detailed pedestrian movement. The predicted reaction time using queueing network was compared with the results from the empirical study to validate the performance of the model. No significant difference between model and empirical results was found with respect to mean reaction time. The second study endeavored to develop a generalized queueing network system so the task can be modeled with the approximated queueing network and its first moment of any service time distribution. There was no significant difference between empirical study results and the proposed model with respect to mean reaction time. Third study investigated methods to quantify pedestrian traffic behavior, and analyze physical and cognitive behavior from the real-world observation and field experiment. Footage from indoor and outdoor corridor was used to quantify pedestrian behavior. Effects of environmental setting and/or psychological factor on travel performance were tested. Finally, adhoc and tailor-made LOS metrics were presented for simple realistic service level assessments. The proposed methodologies were composed of space revision LOS, delay-based LOS, preferred walking speed-based LOS, and ‘blocking probability’

Throughput reduction on the air-ground transport system by the simultaneous effect of multiple traveling routes equipped with parking sites

This paper proposes a stochastic lattice model of the air-ground transport system that comprises a junction of two traveling routes: the domestic route and the international route, each of which has parking sites. The system distributes the arrived aircrafts to either of the two routes and selects one of the parking sites in the route for each aircraft, which stops at the parking site once during its travel. The polyhedral geometric algorithm is introduced to represent inter-vehicular distance and detect other approaching aircrafts. The system displays interesting behavior; remarkably, the dependence of the throughput on the distribution ratio of aircrafts to the domestic route reduces after reaching the maximum parking capacity of the domestic route. Our simulations and analysis with the queueing model describe this phenomenon and suggest the following fact: As the distribution ratio of aircrafts to the international route decreases, the throughput of the international route reduces, and simultaneously, that of the domestic route saturates. This simultaneous effect of the decrease and saturation causes a reduction in the throughput of the entire system

Detection and Simulation of Dangerous Human Crowd Behavior

Tragically, gatherings of large human crowds quite often end in crowd disasters such as the recent catastrophe at the Loveparade 2010. In the past, research on pedestrian and crowd dynamics focused on simulation of pedestrian motion. As of yet, however, there does not exist any automatic system which can detect hazardous situations in crowds, thus helping to prevent these tragic incidents. In the thesis at hand, we analyze pedestrian behavior in large crowds and observe characteristic motion patterns. Based on our findings, we present a computer vision system that detects unusual events and critical situations from video streams and thus alarms security personnel in order to take necessary actions. We evaluate the system’s performance on synthetic, experimental as well as on real-world data. In particular, we show its effectiveness on the surveillance videos recorded at the Loveparade crowd stampede. Since our method is based on optical flow computations, it meets two crucial prerequisites in video surveillance: Firstly, it works in real-time and, secondly, the privacy of the people being monitored is preserved. In addition to that, we integrate the observed motion patterns into models for simulating pedestrian motion and show that the proposed simulation model produces realistic trajectories. We employ this model to simulate large human crowds and use techniques from computer graphics to render synthetic videos for further evaluation of our automatic video surveillance system

Single-file pedestrian dynamics: a review of agent-following models

Single-file dynamics has been studied intensively, both experimentally and theoretically. It shows interesting collective effects, such as stop-and-go waves, which are validation cornerstones for any agent-based modeling approach of traffic systems. Many models have been proposed, e.g. in the form of car-following models for vehicular traffic. These approaches can be adapted for pedestrian streams. In this study, we delve deeper into these models, with particular attention on their interconnections. We do this by scrutinizing the influence of different parameters, including relaxation times, anticipation time, and reaction time. Specifically, we analyze the inherent fundamental problems with force-based models, a classical approach in pedestrian dynamics. Furthermore, we categorize car-following models into stimulus-response and optimal velocity models, highlighting their historical and conceptual differences. These classes can further be subdivided considering the conceptual definitions of the models, e.g. first-order vs. second-order models, or stochastic vs. deterministic models with and without noise. Our analysis shows how car-following models originally developed for vehicular traffic can provide new insights into pedestrian behavior. The focus on single-file motion, which is similar to single-lane vehicular traffic, allows for a detailed examination of the relevant interactions between pedestrians.Comment: 35 pages, 10 Figures; chapter accepted for publication in Crowd Dynamics (vol. 4

Crowd simulation and visualization

Large-scale simulation and visualization are essential topics in areas as different as sociology, physics, urbanism, training, entertainment among others. This kind of systems requires a vast computational power and memory resources commonly available in High Performance Computing HPC platforms. Currently, the most potent clusters have heterogeneous architectures with hundreds of thousands and even millions of cores. The industry trends inferred that exascale clusters would have thousands of millions. The technical challenges for simulation and visualization process in the exascale era are intertwined with difficulties in other areas of research, including storage, communication, programming models and hardware. For this reason, it is necessary prototyping, testing, and deployment a variety of approaches to address the technical challenges identified and evaluate the advantages and disadvantages of each proposed solution. The focus of this research is interactive large-scale crowd simulation and visualization. To exploit to the maximum the capacity of the current HPC infrastructure and be prepared to take advantage of the next generation. The project develops a new approach to scale crowd simulation and visualization on heterogeneous computing cluster using a task-based technique. Its main characteristic is hardware agnostic. It abstracts the difficulties that imply the use of heterogeneous architectures like memory management, scheduling, communications, and synchronization — facilitating development, maintenance, and scalability. With the goal of flexibility and take advantage of computing resources as best as possible, the project explores different configurations to connect the simulation with the visualization engine. This kind of system has an essential use in emergencies. Therefore, urban scenes were implemented as realistic as possible; in this way, users will be ready to face real events. Path planning for large-scale crowds is a challenge to solve, due to the inherent dynamism in the scenes and vast search space. A new path-finding algorithm was developed. It has a hierarchical approach which offers different advantages: it divides the search space reducing the problem complexity, it can obtain a partial path instead of wait for the complete one, which allows a character to start moving and compute the rest asynchronously. It can reprocess only a part if necessary with different levels of abstraction. A case study is presented for a crowd simulation in urban scenarios. Geolocated data are used, they were produced by mobile devices to predict individual and crowd behavior and detect abnormal situations in the presence of specific events. It was also address the challenge of combining all these individual’s location with a 3D rendering of the urban environment. The data processing and simulation approach are computationally expensive and time-critical, it relies thus on a hybrid Cloud-HPC architecture to produce an efficient solution. Within the project, new models of behavior based on data analytics were developed. It was developed the infrastructure to be able to consult various data sources such as social networks, government agencies or transport companies such as Uber. Every time there is more geolocation data available and better computation resources which allow performing analysis of greater depth, this lays the foundations to improve the simulation models of current crowds. The use of simulations and their visualization allows to observe and organize the crowds in real time. The analysis before, during and after daily mass events can reduce the risks and associated logistics costs.La simulación y visualización a gran escala son temas esenciales en áreas tan diferentes como la sociología, la física, el urbanismo, la capacitación, el entretenimiento, entre otros. Este tipo de sistemas requiere una gran capacidad de cómputo y recursos de memoria comúnmente disponibles en las plataformas de computo de alto rendimiento. Actualmente, los equipos más potentes tienen arquitecturas heterogéneas con cientos de miles e incluso millones de núcleos. Las tendencias de la industria infieren que los equipos en la era exascale tendran miles de millones. Los desafíos técnicos en el proceso de simulación y visualización en la era exascale se entrelazan con dificultades en otras áreas de investigación, incluidos almacenamiento, comunicación, modelos de programación y hardware. Por esta razón, es necesario crear prototipos, probar y desplegar una variedad de enfoques para abordar los desafíos técnicos identificados y evaluar las ventajas y desventajas de cada solución propuesta. El foco de esta investigación es la visualización y simulación interactiva de multitudes a gran escala. Aprovechar al máximo la capacidad de la infraestructura actual y estar preparado para aprovechar la próxima generación. El proyecto desarrolla un nuevo enfoque para escalar la simulación y visualización de multitudes en un clúster de computo heterogéneo utilizando una técnica basada en tareas. Su principal característica es que es hardware agnóstico. Abstrae las dificultades que implican el uso de arquitecturas heterogéneas como la administración de memoria, las comunicaciones y la sincronización, lo que facilita el desarrollo, el mantenimiento y la escalabilidad. Con el objetivo de flexibilizar y aprovechar los recursos informáticos lo mejor posible, el proyecto explora diferentes configuraciones para conectar la simulación con el motor de visualización. Este tipo de sistemas tienen un uso esencial en emergencias. Por lo tanto, se implementaron escenas urbanas lo más realistas posible, de esta manera los usuarios estarán listos para enfrentar eventos reales. La planificación de caminos para multitudes a gran escala es un desafío a resolver, debido al dinamismo inherente en las escenas y el vasto espacio de búsqueda. Se desarrolló un nuevo algoritmo de búsqueda de caminos. Tiene un enfoque jerárquico que ofrece diferentes ventajas: divide el espacio de búsqueda reduciendo la complejidad del problema, puede obtener una ruta parcial en lugar de esperar a la completa, lo que permite que un personaje comience a moverse y calcule el resto de forma asíncrona, puede reprocesar solo una parte si es necesario con diferentes niveles de abstracción. Se presenta un caso de estudio para una simulación de multitud en escenarios urbanos. Se utilizan datos geolocalizados producidos por dispositivos móviles para predecir el comportamiento individual y público y detectar situaciones anormales en presencia de eventos específicos. También se aborda el desafío de combinar la ubicación de todos estos individuos con una representación 3D del entorno urbano. Dentro del proyecto, se desarrollaron nuevos modelos de comportamiento basados ¿¿en el análisis de datos. Se creo la infraestructura para poder consultar varias fuentes de datos como redes sociales, agencias gubernamentales o empresas de transporte como Uber. Cada vez hay más datos de geolocalización disponibles y mejores recursos de cómputo que permiten realizar un análisis de mayor profundidad, esto sienta las bases para mejorar los modelos de simulación de las multitudes actuales. El uso de simulaciones y su visualización permite observar y organizar las multitudes en tiempo real. El análisis antes, durante y después de eventos multitudinarios diarios puede reducir los riesgos y los costos logísticos asociadosPostprint (published version