Impact of passenger group dynamics on an airport evacuation process using an agent-based model

The safety of passengers is a major concern to airports. In the event of crises, having an effective and efficient evacuation process in place can significantly aid in enhancing passenger safety. Hence, it is necessary for airport operators to have an in-depth understanding of the evacuation process of their airport terminal. Although evacuation models have been used in studying pedestrian behaviour for decades, little research has been done in considering the evacuees’ group dynamics and the complexity of the environment. In this paper, an agent-based model is presented to simulate passenger evacuation process. Different exits were allocated to passengers based on their location and security level. The simulation results show that the evacuation time can be influenced by passenger group dynamics. This model also provides a convenient way to design airport evacuation strategy and examine its efficiency. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

Agent-based modelling simulation case study : assessment of airport check-in and evacuation process by considering group travel behaviour of air passengers

Passenger experience has become a major factor that influences the success of an airport. In this context, passenger flow simulation has been used in designing and managing airports. However, most passenger flow simulations failed to consider the group dynamics when developing passenger flow models. In this paper, an agent-based model is presented to simulate passenger behaviour at the airport check-in and evacuation process. The simulation results show that the passenger behaviour can have significant influences on the performance and utilisation of services in airport terminals. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

Analysis of passenger group behaviour and its impact on passenger flow using an agent-based model

Group interaction within crowds is a common phenomenon and has great influence on pedestrian behaviour. This paper investigates the impact of passenger group dynamics using an agent-based simulation method for the outbound passenger process at airports. Unlike most passenger-flow models that treat passengers as individual agents, the proposed model additionally incorporates their group dynamics as well. The simulation compares passenger behaviour at airport processes and discretionary services under different group formations. Results from experiments (both qualitative and quantitative) show that incorporating group attributes, in particular, the interactions with fellow travellers and wavers can have significant influence on passengers activity preference as well as the performance and utilisation of services in airport terminals. The model also provides a convenient way to investigate the effectiveness of airport space design and service allocations, which can contribute to positive passenger experiences. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

Object oriented simulation of hybrid renewable energy systems focused on supervisor control

EFTA 2009

Using the system dynamics paradigm in teaching and learning technological university subjets

2nd International Conference on Education and New Learning Technologies

Complete agent based simulation of mini-grids

EuroPES 2009

Modelo de Alocação de Mamógrafos Móveis: Um estudo para a Região Serrana do Estado do Rio de Janeiro.

O Estado do Rio de Janeiro possui indicadores de produção muito baixos na realização de exames de câncer de mama. Na tentativa de melhorar o acesso aos exames, principalmente em regiões com baixa densidade populacional onde a aquisição de mamógrafos não é custo-efetiva, o uso da mamografia móvel é uma alternativa para aumentar a execução de exames de rastreamento de câncer de mama. O objetivo desta pesquisa é a construção de um modelo computacional para definir a alocação de mamógrafos móveis. O Modelo considera as variáveis associadas com os custos e prazos, indicando quando, onde e por quanto tempo, as unidades móveis de mamografia devem permanecer em cada cidade. O modelo foi construído no software de modelagem e simulação Anylogic, usando técnicas de modelagem baseada em agentes. O principal resultado é determinar o percurso de cada veículo disponível, para oferecer a cobertura desejada em cada cidade. Todas as entradas são parametrizadas, permitindo simular diferentes cenários e fornecer informações importantes para o processo de tomada de decisão. O horizonte de tempo, número de mamógrafos (fixos e móveis), a cobertura desejada da população, a capacidade de produção de cada dispositivo, a adesão da população urbana e rural, entre outras variáveis, foram consideradas no modelo. Os dados da Região Serrana do Rio de Janeiro foram usados nas simulações, onde menos de metade das cidades possuem mamógrafos fixos. Com o modelo proposto foi possível determinar a distribuição de cada dispositivo físico e o número ótimo de unidades móveis de mamografia para oferecer cobertura à totalidade da população no ciclo de dois anos. O número de mamógrafos para oferecer cobertura de toda a população da região poderia ser reduzido pela metade com o modelo de alocação proposto neste trabalho. A utilização de mamografia móvel, em conjunto com a rede existente de mamógrafos fixos, procura maximizar a disponibilização de exames de testes de diagnóstico de câncer de mama no estado do Rio de Janeiro. O desenvolvimento de um modelo de roteamento que aperfeiçoa a cobertura de rastreio do câncer de mama é apresentado como um complemento importante na tentativa de melhorar o acesso à população residente em áreas urbanas e rurais dos municípios.

An Evaluation of Complex Adaptive Evolvable System Simulation

Thesis (Master's)--University of Washington, 2016-03

An Agent-based Classification Model

The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.