Integrating a Human Behavior Model within an Agent-Based Approach for Blasting Evacuation

Several studies on Emergency Management are available in the literature, but most of them do not consider how the human behavior during an emergency can affect the evacuation process. Therefore, the novel contribution of this article is the implementation of an agent‐based model to describe the evacuation, due to a blast in a public area, integrated with a human behavior analytical model. Each agent has its own behavior that is described in a layered framework. The first layer simulates the “agent's features” function. Then, an “individual module” describes dynamically the emotional aspects using (i) the Decision Field Theory, (ii) a stationary stochastic model, and (iii) the results coming from a questionnaire. An agent‐based model with integrated human behavior is proposed to test critical infrastructures in emergency conditions without performing full scale evacuation tests. Analyses could be performed both in real time with a hazard scenario and at the design level to predict the system response to identify the optimal configuration. Therefore, the development of the proposed methodology could support both designers and policy makers in the decision‐making process

A decision framework for managing the risk of terrorist threats at rail stations interconnected with airports

This paper highlights a risk-based decision-making framework on a basis of probabilistic risk assessment (PRA). Its aim is to enable stakeholders of transport infrastructures to systematically and effectively allocate their limited resources and consequently improve resilience when facing the potential risk of a terrorist attack. The potential risk of a terrorist attack affects the inter-operation of transportation infrastructures including airports and rail stations, the regional economy, and imposes additional costs of security or any countermeasures. This novel framework is thus established in order to model the security system, to consider a multitude of threat scenarios, and to assess the decisions and choices taken by the aggressors during various stages of their attack. The framework has capability to identify the state of partial neutralization, which reveals the losses incurred when the terrorist could not reach the primary target. In this study, an underground railway station interconnected to an international airport has been used as a case study to demonstrate the effectiveness of this novel framework. By the rigorous assessment of potential losses during a variety of threat scenarios, four countermeasures that could minimise losses are proposed: screening of passengers by observation techniques (SPOT), a surveillance system, increase of the cargo screening rate, and blast-resistant cargo containers. The cost and efficiency assessment is employed to determine the most suitable countermeasures when the value of the security measures equal their cost. Note that ongoing research is still needed to establish better countermeasures since there is no end to the creativity of terrorists. The new technology, such as wireless sensors, will play an important role in the security system in the future. In particular, this study will help insurance and rail industries to model and manage risk profiles at critical infrastructure

Enhancing Evacuation Planning in Public Buildings: Optimising Egress Location and Protection

Effective evacuation strategies are crucial for ensuring the safety of individuals during emergencies and disasters. Despite significant progress in evacuation planning, the intricate dynamics of disaster scenarios and uncertainties inherent in such situations need to be better incorporated in planning egress locations to enhance safety in buildings. This work focuses on strategically locating egress points within public buildings, acknowledging their pivotal role in facilitating secure evacuations. Optimising egress points improves evacuation efficiency and minimises associated risks, significantly improving evacuation. This research introduces an innovative approach that integrates optimisation models, addresses decision-making complexities, explores practical applications, and considers potential attack scenarios. The study explores evacuation dynamics across diverse scenarios, elevating preparedness, and safety protocols to protect public assets and lives. Developing mixedinteger programming models establishes a foundation for optimising egress locations. MCDM is then employed, leveraging the F-AHP to address uncertainties in egress selection. Practicality is realised through integrating Revit and AnyLogic software, facilitating assessment through BIM and ABM. A stochastic BP model is formulated, addressing both Defender and Attacker perspectives for enhanced egress strategies. This model strategically allocates resources to fortify egresses, ensuring occupant safety during evacuations. Contributions further optimisation approaches, fortification strategies, and progressive enhancements in evacuation planning. These collectively address key challenges and gaps in existing literature, enhancing evacuation efficiency and public safety during emergencies. The research bridges gaps in existing approaches, providing a framework for future investigations into optimising evacuation strategies, enhanced disaster preparation, and further advancements in the field

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly