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.

Dynamic agent composition for large-scale agent-based models

PURPOSE: This paper describes dynamic agent composition, used to support the development of flexible and extensible large-scale agent-based models (ABMs). This approach was motivated by a need to extend and modify, with ease, an ABM with an underlying networked structure as more information becomes available. Flexibility was also sought after so that simulations are set up with ease, without the need to program. METHODS: The dynamic agent composition approach consists in having agents, whose implementation has been broken into atomic units, come together at runtime to form the complex system representation on which simulations are run. These components capture information at a fine level of detail and provide a vast range of combinations and options for a modeller to create ABMs. RESULTS: A description of the dynamic agent composition is given in this paper, as well as details about its implementation within MODAM (MODular Agent-based Model), a software framework which is applied to the planning of the electricity distribution network. Illustrations of the implementation of the dynamic agent composition are consequently given for that domain throughout the paper. It is however expected that this approach will be beneficial to other problem domains, especially those with a networked structure, such as water or gas networks. CONCLUSIONS: Dynamic agent composition has many advantages over the way agent-based models are traditionally built for the users, the developers, as well as for agent-based modelling as a scientific approach. Developers can extend the model without the need to access or modify previously written code; they can develop groups of entities independently and add them to those already defined to extend the model. Users can mix-and-match already implemented components to form large-scales ABMs, allowing them to quickly setup simulations and easily compare scenarios without the need to program. The dynamic agent composition provides a natural simulation space over which ABMs of networked structures are represented, facilitating their implementation; and verification and validation of models is facilitated by quickly setting up alternative simulations.

An agent-based approach to immune modelling

This study focuses on trying to understand why the range of experience with respect to HIV infection is so diverse, especially as regards to the latency period. The challenge is to determine what assumptions can be made about the nature of the experience of antigenic invasion and diversity that can be modelled, tested and argued plausibly. To investigate this, an agent-based approach is used to extract high-level behaviour which cannot be described analytically from the set of interaction rules at the cellular level. A prototype model encompasses local variation in baseline properties contributing to the individual disease experience and is included in a network which mimics the chain of lymphatic nodes. Dealing with massively multi-agent systems requires major computational efforts. However, parallelisation methods are a natural consequence and advantage of the multi-agent approach. These are implemented using the MPI library.

Considerations for a social and geographical framework for agent-based epidemics

Understanding the dynamics of disease spread is of crucial importance, in contexts such as estimating load on medical services to risk assessment and intervention policies against large-scale epidemic outbreaks. However, most of the information is available after the spread itself, and preemptive assessment is far from trivial. Here, we investigate the use of agent-based simulations to model such outbreaks in a stylised urban environment. For most diseases, infection of a new individual may occur from casual contact in crowds as well as from repeated interactions with social partners such as work colleagues or family members. Our model therefore accounts for these two phenomena.Presented in this paper is the initial framework for such a model, detailing implementation of geographical features and generation of social structures. Preliminary results are a promising step towards large-scale simulations and evaluation of potential intervention policies.

Model refinement through high-performance computing: an agent-based HIV example

Background Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infection, and the massive cell depletion associated with it may influence subsequent disease progression. This motivated the development of a large-scale agent-based model. Results Lymph nodes are explicitly implemented, and considerations on parallel computing permit large simulations and the inclusion of local features. The results obtained show that GI tract inclusion in the model leads to an accelerated disease progression, during both the early stages and the long-term evolution, compared to a theoretical, uniform model. Conclusions These results confirm the potential of treatment policies currently under investigation, which focus on this region. They also highlight the potential of this modelling framework, incorporating both agent-based and network-based components, in the context of complex systems where scaling-up alone does not result in models providing additional insights.

Agent-based modelling of viral infection

The three phases of the macroscopic evolution of the HIV infection are well known, but it is still difficult to understand how the cellular-level interactions come together to create this characteristic pattern and, in particular, why there are such differences in individual responses. An 'agent-based' approach is chosen as a means of inferring high-level behaviour from a small set of interaction rules at the cellular level. Here the emphasis is on cell mobility and viral mutations.

A parallel approach to social network generation and agent-based epidemic simulation

Understanding the dynamics of disease spread is essential in contexts such as estimating load on medical services, as well as risk assessment and interven- tion policies against large-scale epidemic outbreaks. However, most of the information is available after the outbreak itself, and preemptive assessment is far from trivial. Here, we report on an agent-based model developed to investigate such epidemic events in a stylised urban environment. For most diseases, infection of a new individual may occur from casual contact in crowds as well as from repeated interactions with social partners such as work colleagues or family members. Our model therefore accounts for these two phenomena. Given the scale of the system, efficient parallel computing is required. In this presentation, we focus on aspects related to paralllelisation for large networks generation and massively multi-agent simulations.

Decentralised Communication in Autonomous Agent Swarms

Communication and information diffusion are typically difficult in situations where centralised structures may become unavailable. In this context, decentralised communication based on epidemic broadcast becomes essential. It can be seen as an opportunity-based flooding for message broadcasting within a swarm of autonomous agents, where each entity tries to share the information it possesses with its neighbours. As an example of applications for such a system, we present simulation results where agents have to coordinate to map an unknown area.

Agent-based modelling and simulation of airplane boarding processes

Passenger flow simulations are an important tool for designing and managing airports. This thesis examines the different boarding strategies for the Boeing 777 and Airbus 380 aircraft in order to investigate their current performance and to determine minimum boarding times. The most optimal strategies have been discovered and new strategies that are more efficient are proposed. The methods presented offer reduced aircraft boarding times which plays an important role for reducing the overall aircraft Turn Time for an airline.

Comparative analysis reveals loss of the appetite-regulating peptide hormone ghrelin in falcons

Ghrelin and leptin are key peripherally secreted appetite-regulating hormones in vertebrates. Here we consider the ghrelin gene (GHRL) of birds (class Aves), where it has been reported that ghrelin inhibits rather than augments feeding. Thirty-one bird species were compared, revealing that most species harbour a functional copy of GHRL and the coding region for its derived peptides ghrelin and obestatin. We provide evidence for loss of GHRL in saker and peregrine falcons, and this is likely to result from the insertion of an ERVK retrotransposon in intron 0. We hypothesise that the loss of anorexigenic ghrelin is a predatory adaptation that results in increased food-seeking behaviour and feeding in falcons.

Regulating ride-sharing in the peer economy

The rise of the peer economy poses complex new regulatory challenges for policy-makers. The peer economy, typified by services like Uber and AirBnB, promises substantial productivity gains through the more efficient use of existing resources and a marked reduction in regulatory overheads. These services are rapidly disrupting existing established markets, but the regulatory trade-offs they present are difficult to evaluate. In this paper, we examine the peer economy through the context of ride-sharing and the ongoing struggle over regulatory legitimacy between the taxi industry and new entrants Uber and Lyft. We first sketch the outlines of ride-sharing as a complex regulatory problem, showing how questions of efficiency are necessarily bound up in questions about levels of service, controls over pricing, and different approaches to setting, upholding, and enforcing standards. We outline the need for data-driven policy to understand the way that algorithmic systems work and what effects these might have in the medium to long term on measures of service quality, safety, labour relations, and equality. Finally, we discuss how the competition for legitimacy is not primarily being fought on utilitarian grounds, but is instead carried out within the context of a heated ideological battle between different conceptions of the role of the state and private firms as regulators. We ultimately argue that the key to understanding these regulatory challenges is to develop better conceptual models of the governance of complex systems by private actors and the available methods the state has of influencing their actions. These struggles are not, as is often thought, struggles between regulated and unregulated systems. The key to understanding these regulatory challenges is to better understand the important regulatory work carried out by powerful, centralised private firms – both the incumbents of existing markets and the disruptive network operators in the peer-economy.

Regulating ride-sharing in the peer economy

The rise of the peer economy poses complex new regulatory challenges for policy-makers. The peer economy, typified by services like Uber and AirBnB, promises substantial productivity gains through the more efficient use of existing resources and a marked reduction in regulatory overheads. These services are rapidly disrupting existing established markets, but the regulatory trade-offs they present are difficult to evaluate. In this paper, we examine the peer economy through the context of ride-sharing and the ongoing struggle over regulatory legitimacy between the taxi industry and new entrants Uber and Lyft. We first sketch the outlines of ride-sharing as a complex regulatory problem, showing how questions of efficiency are necessarily bound up in questions about levels of service, controls over pricing, and different approaches to setting, upholding, and enforcing standards. We outline the need for data-driven policy to understand the way that algorithmic systems work and what effects these might have in the medium to long term on measures of service quality, safety, labour relations, and equality. Finally, we discuss how the competition for legitimacy is not primarily being fought on utilitarian grounds, but is instead carried out within the context of a heated ideological battle between different conceptions of the role of the state and private firms as regulators. We ultimately argue that the key to understanding these regulatory challenges is to develop better conceptual models of the governance of complex systems by private actors and the available methods the state has of influencing their actions. These struggles are not, as is often thought, struggles between regulated and unregulated systems. The key to understanding these regulatory challenges is to better understand the important regulatory work carried out by powerful, centralised private firms – both the incumbents of existing markets and the disruptive network operators in the peer-economy.

Magnetic nanoparticles boosting the osmotic efficiency of a polymeric FO draw agent: Effect of polymer conformation

Poly sodium acrylate (PSA)-coated Magnetic Nanoparticles (PSA-MNPs) were synthesized as smart osmotic draw agent (SMDA) for water desalination by forward osmosis (FO) process. The PSA-coated MNPs demonstrated significantly higher osmotic pressure (~ 30 fold) as well as high FO water flux (~ 2–3 fold) when compared to their polymer (polyelectrolyte) counterpart, even at a very low concentration of ~ 0.13 wt.% in the draw solution. The PSA polymer chain conformation – coiled to extended – demonstrates a significant impact on the availability of the polymer hydrophilic groups in solution which is the driving force to attain higher osmotic pressure and water flux. When an optimum concentration of the polymer was anchored to a NP surface, the polymer chains assume an extended open conformation making the functional hydrophilic groups available to attract water molecules. This in turn boosts the osmotic pressure and FO water flux of the PSA-MNP draw agents. The low concentration of the PSA-MNP osmotic agent and the associated high water flux enhances the cost-effectiveness of our proposed SMDA system. In addition, easier magnetic separation and regeneration of the SMDA also improves its usability making it efficient, cost-effective and environment-friendly.

Failure to launch: The self-regulating Md -MYB10R6 gene from apple is active in flowers but not leaves of Petunia

Coloured foliage due to anthocyanin pigments (bronze/red/black) is an attractive trait that is often lacking in many bedding, ornamental and horticultural plants. Apples (Malus × domestica) containing an allelic variant of the anthocyanin regulator, Md-MYB10R6, are highly pigmented throughout the plant, due to autoregulation by MYB10 upon its own promoter. We investigated whether Md-MYB10R6 from apple is capable of functioning within the heterologous host Petunia hybrida to generate plants with novel pigmentation patterns. The Md-MYB10R6 transgene (MYB10–R6pro:MYB10:MYB10term) activated anthocyanin synthesis when transiently expressed in Antirrhinumroseadorsea petals and petunia leaf discs. Stable transgenic petunias containing Md-MYB10R6 lacked foliar pigmentation but had coloured flowers, complementing the an2 phenotype of ‘Mitchell’ petunia. The absence of foliar pigmentation was due to the failure of the Md-MYB10R6 gene to self-activate in vegetative tissues, suggesting that additional protein partners are required for Md-MYB10 to activate target genes in this heterologous system. In petunia flowers, where endogenous components including MYB-bHLH-WDR (MBW) proteins were present, expression of the Md-MYB10R6 promoter was initiated, allowing auto-regulation to occur and activating anthocyanin production. Md-MYB10 is capable of operating within the petunia MBW gene regulation network that controls the expression of the anthocyanin biosynthesis genes, AN1 (bHLH) and MYBx (R3-MYB repressor) in petals.

Agent-based simulation as a component of mixed methods in entrepreneurship research

As a new research method supplementing the existing qualitative and quantitative approaches, agent-based modelling and simulation (ABMS) may fit well within the entrepreneurship field because the core concepts and basic premises of entrepreneurship coincide with the characteristics of ABMS (McKelvey, 2004; Yang & Chandra, 2013). Agent-based simulation is a simulation method based on agent-based models. The agentbased models are composed of heterogeneous agents and their behavioural rules. By repeatedly carrying out agent-based simulations on a computer, the simulations reproduce each agent’s behaviour, their interactive process, and the emerging macroscopic phenomenon according to the flow of time. Using agent-based simulations, researchers may investigate temporal or dynamic effects of each agent’s behaviours.