HPC applications for data-driven agent-based models of pedestrian movement

La gestió de grans instal·lacions multiús és un procés complicat, que té a veure en trobar un equilibri entre la satisfacció del client, aspectes de seguretat i els interessos comercials. Aquest repte s'accentua en períodes de transició, com èpoques de construcció o la millora i manteniment de la instal·lació. Tot i això, amb el creixement de l'Internet of Things(IoT) i l'accés a HPC per a usos comercials als darrers anys ha proporcionat una manera d'adreçar aquest repte a través d'aquestes tecnologies. Les dades provinents d'aquests sistemes fortament monitorats, combinats amb IA i tècniques de simulació, permeten una nova manera d'abordar la gestió de grans instal·lacions. En aquest Treball de Fi de Grau s'ha desenvolupat un Digital Twin del recinte insígnia del Futbol Club Barcelona: el Camp Nou. L'aspecte més important en el funcionament del recinte són els fluxos de vianants i la seva optimització, assegurar un pla robust enfront de les emergències i gestionar els canvis relacionats amb el projecte de construcció que consisteix en la renovació del recinte del Camp Nou també són prioritaris. Aquest prototip de Model de Moviment de Vianants mostra la viabilitat de combinar diverses fonts de dades amb l'objectiu de representar diversos escenaris relacionats amb la gestió de multituds. Aquest model servirà com a referència per integrar les dades provinents de sensors i preprocessades utilitzant tècniques de Machine Learning. Aquest model estarà integrat amb l'estructura de tot IoTwins dins els test-beds 5 i 11 que se centren en les instal·lacions del FCB. El mètode escollit per la simulació del projecte és la Simulació Basada en Agents. Un paradigma de simulació cada vegada més popular que és capaç de representar poblacions heterogènies que estan formades per agents individuals que representen els vianants, per exemple. El seu moviment està definit per un algoritme desenvolupat específicament que representa l'espai al voltant dels Agents de manera matemàtica, derivada d'una funció de cost que combina diferents factors que afecten els vianants. Els Agents entren al recinte, es mouen seguint les seves prioritats i després en surten seguint el seu camí individual. El model es validarà i calibrarà amb les dades accessibles en aquest moment. Diversos escenaris d'exemple han demostrat la viabilitat del model per optimitzar l'evacuació en cas d'emergència i les afectacions que comporten les renovacions del recinte.The management of large multinational facilities is a complex process involving finding the balance between customer satisfaction, safety concerns and commercial interests. This challenge is particularly pronounced in periods of transitions, such as stages construction work, facility upgrade and maintenance. However, with the growth of the Internet of Things (IoT) and unlocking of HPC for commercial endeavours in recent years offers to address this challenge through the use of technology. The increasing amount of data coming from these heavily monitored system, combined with AI and simulation techniques offers a new approach to the management of large facilities. In this Bachelor Thesis' a Digital Twin of the Football Club Barcelona flagship sports venue: the Camp Nou has been developed. The most important aspect in the functioning of the venue are pedestrian flows and optimising them, ensuring robust emergency planning and managing change related to phased construction project involving a full renovation of the Camp Nou precinct are the main priorities. This prototype of Pedestrian Movement Model shows the feasibility of combining various data streams to represent multiple scenarios of crowd management. This model will serve as a baseline for integrating data coming from a number of sensors and preprocessed with Machine Learning techniques. This model will be integrated as a part of the whole IoTwins structure of test-beds 5 and 11 that focus on the FCB facilities. The approach taken in the simulation part of the project is Agent Based Modelling. An increasingly popular simulation technique that is able to represent heterogeneous population consisting of individual Agents, for example, representing pedestrians. Their movement is defined with an specially developed algorithm that represent the space around the Agents as a mathematically derived cost function that combines multiple factors affecting the movement of pedestrians. The Agents enter the precinct, move around it and leave it following their independent paths. The model is validated and calibrated using currently available data. Several example scenarios have been run to show the feasibility of the approach for optimising emergency evacuation and construction-caused disruptions to normal operations

Data Assimilation for Spatial Temporal Simulations Using Localized Particle Filtering

As sensor data becomes more and more available, there is an increasing interest in assimilating real time sensor data into spatial temporal simulations to achieve more accurate simulation or prediction results. Particle Filters (PFs), also known as Sequential Monte Carlo methods, hold great promise in this area as they use Bayesian inference and stochastic sampling techniques to recursively estimate the states of dynamic systems from some given observations. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. As the state space dimension increases, the number of particles must increase exponentially in order to converge to the true system state. The purpose of this dissertation work is to develop localized particle filtering to support PFs-based data assimilation for large-scale spatial temporal simulations. We develop a spatially dependent particle-filtering framework that breaks the system state and observation data into sub-regions and then carries out localized particle filtering based on these spatial regions. The developed framework exploits the spatial locality property of system state and observation data, and employs the divide-and-conquer principle to reduce state dimension and data complexity. Within this framework, we propose a two-level automated spatial partitioning method to provide optimized and balanced spatial partitions with less boundary sensors. We also consider different types of data to effectively support data assimilation for spatial temporal simulations. These data include both hard data, which are measurements from physical devices, and soft data, which are information from messages, reports, and social network. The developed framework and methods are applied to large-scale wildfire spread simulations and achieved improved results. Furthermore, we compare the proposed framework to existing particle filtering based data assimilation frameworks and evaluate the performance for each of them

Pedestrian steering behaviour modelling within the built environment

Prediction of pedestrians’ steering behaviours within the built environments under normal and non-panic situations is useful for a wide range of applications, which include social science, psychology, architecture, and computer graphics. The main focus is on prediction of the pedestrian walking paths and the influences from the surrounding environment from the engineering point of view