Ander García will defend his thesis on Thursday, April 23rd

The defence will take place at Sala Adela Moyua, Faculty of Science and Technology (EHU - Leioa) at 11:30

Ander García is a researcher in the Computational Mathematics – Modeling and Simulation group at BCAM. His work focuses on the analysis and development of pedestrian dynamics models for real-world applications, including crowd management, overcrowding analysis, and pedestrian flow studies. He holds a Bachelor's degree in Physics and a Master's degree in Quantum Science and Technology, both from the University of the Basque Country (EHU). After a six-month internship at BCAM, where he conducted preliminary simulations of pedestrian flows in part of San Mamés, he began his PhD in September 2021 within the Mathematics and Statistics program. During his time at BCAM, he has contributed to several projects, including M³OVE (Technology Transfer Program 2023), which focused on the development of numerical software for large-scale event organization, with a final application to the analysis of crowd management protocols used during international matches at San Mamés; and DigiCasco (Technology Transfer Program 2025), centered on the analysis and application of pedestrian dynamics simulations in Bilbao’s Casco Viejo under multiple flow conditions. In addition, his research extends to the field of infection dynamics, integrating it with pedestrian models to study COVID-19 transmission at the microscopic scale. In this context, he is currently applying this combined pedestrian–infection modeling approach at Galdakao-Usansolo Hospital to investigate nosocomial COVID-19 propagation.

His thesis, titled “Pedestrian Dynamics Simulation Framework for Real-World Applications in Complex Public Environments” is supervised by Prof. Marco Ellero (BCAM & Ikerbasque) and Dr. Dae-Jin Lee (IE University). It is scheduled to be defended on April 23rd, 2026, at Sala Adela Moyua, Faculty of Science and Technology (EHU - Leioa) at 11:30 a.m. 

On behalf of all members of BCAM, we would like to wish him all the best for the future, both professionally and personally.

Abstract

This dissertation develops a robust pedestrian-dynamics simulation framework for real-life applications in complex public environments. Managing pedestrian flows has become increasingly critical in large cities and during mass events, where overcrowding can result in severe safety risks. The proposed framework enables the identification of high-density zones and the reproduction of complex pedestrian behaviors relevant for crowd management. In addition, the work addresses a contemporary challenge intensified by the COVID-19 pandemic: disease transmission in crowded indoor spaces. To this end, a novel infection-dynamics model is introduced, incorporating key aerosol-based transmission mechanisms such as anisotropic emission and inhalation, spatial diffusion, and viral decay in the air. The integrated pedestrian–infection model is applied to assess nosocomial infections in a hospital, incorporating real-life information on healthcare personnel work shifts.

For pedestrian-dynamics modeling, this dissertation adopts the widely used Social Force Model. A comprehensive examination of the model is conducted to evaluate the impact of force formulations on pedestrian behavior, and to overcome its reduced capabilities by enabling the reproduction of key macroscopic empirical datasets while preserving collision-avoidance performance and emergent collective patterns reported in earlier implementations. Subsequently, the navigation in complex geometries is addressed by calculating time-independent vector fields to guide pedestrians towards intermediate and final targets within simulated environments. Further extensions are introduced to capture complex behaviors relevant in real-world environments, namely queue formation and route choice in multi-exit settings. By identifying queuing pedestrians based on a cutoff radius and adjusting the dynamics of queuing agents, a wide range of formations are modeled, thus enhancing existing approaches. Building upon this queuing mechanism, a time-use efficiency algorithm is introduced to account for delays caused by queues and to replicate pedestrian decision-making. This method successfully reproduces route-choice behavior observed in independent experimental datasets featuring multiple exits, diverse geometries, and varying participant groups.

The extended pedestrian-simulation methodology is ultimately applied to model spectator ingress at San Mamés Stadium. In this scenario, about 50,000 agents move around the stadium to reach one of 24 entry gates, generating diverse flow conditions, including unidirectional and bidirectional streams, sparse and dense regions, and queue formation. The model is used to evaluate the crowd-management protocols implemented during the 2024–2025 UEFA Europa League matches. The empirical access times at the turnstiles are accurately reproduced, yielding realistic pedestrian dynamics around the venue. This enables the identification of high-density areas associated with each management strategy and allows assessment of the impact of different arrival patterns and flow rates on local congestion levels.

Finally, infection dynamics in crowds are investigated using a novel approach based on discretizing the diffusion equation via the smoothed-particle hydrodynamics (SPH) method. The anisotropic nature of human exhalation and inhalation is modeled by accounting for the relative face orientation between infected and susceptible individuals during direct transmission, which occurs within a 2 m radius of an infectious agent. Long-range indirect transmission is incorporated through spatial diffusion of viral particles in the air, together with exponential viral decay. After analyzing the influence of pedestrian mobility on the propagation dynamics, the integrated pedestrian–infection framework is applied to the 9B floor of Galdakao-Usansolo Hospital to study nosocomial COVID-19 transmission under routine daily conditions. The model assesses the impact of management interventions, such as reinforcing staff or controlling visits, on virus spread, assuming multiple sources of infection: patients, healthcare personnel, and visitors.