Towards Agent-Based Crowd Simulation in Airports Using Games Technology

Szymanezyk, Olivier; Dickinson, Patrick; Duckett, Tom

doi:10.1007/978-3-642-22000-5_54

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…Advanced behaviour altering aspects such as agent learning [11], social forces models [12], physiological factors [13] and sociological effects [14] can also be utilised to enhance agent believability. Technologies from the games industry and middleware have been deployed for simulating crowds within a variety of different scenarios, to allow for unique research into specific areas such as safety [2]. The crowd simulation produced as part of this research follows the general build by utilising decision making, pathfinding, steering and perception along with an additional feature, behavioural annotation.…”

Section: A Crowd Simulation Techniquesmentioning

confidence: 98%

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A perceptual study into the behaviour of autonomous agents within a virtual urban environment

O’Connor

Liarokapis

Peters

2013

2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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Simulating vast crowds of autonomous agents within a procedurally generated virtual environment is a challenging endeavour from a technical perspective, however it becomes even more difficult when the subjective nature of perception is also taken into account. Agent behaviour is the product of artificial intelligence systems working in tandem, however the sophistication of these systems is not a guarantee of achieving believable behaviour. Within locations based upon reality such as an urban environment, the perceived realism of agent behaviour becomes even harder to achieve. This paper presents the development of a crowd simulation that is based upon a real-life urban environment, which is then subjected to perceptual experimentation to identify features of behaviour which can be linked to perceived realism. This research is predicted to feedback into the development processes of inhabited cities, especially those attempting to simulate perceptually realistic agents as it will highlight features of behaviour that are important to implement. The perceptual experimentation methodologies presented can also be adapted and potentially utilised to test other types of crowd simulation, whether it be for the purposes of computer games or even urban planning and health and safety.

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Section: A Crowd Simulation Techniquesmentioning

confidence: 98%

“…A virtual scene is generally based upon a real-life counterpart in some way, shape, or form [2]. A famous example for crowd simulation is the Shibuya Crossing in Tokyo.…”

Section: Introductionmentioning

confidence: 99%

A perceptual study into the behaviour of autonomous agents within a virtual urban environment

O’Connor

Liarokapis

Peters

2013

2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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“…In recent years, ABM is seen as a powerful tool that allows for the explicit representation of the processes underlying the large-scale animal movement patterns [64][65][66][67][68][69][70]. They are also popular in pedestrian modelling [71][72][73][74][75][76][77][78], in experimenting the effects of movement behaviours on epidemic spread [79,80], and in crowd movement simulation [81,82]. This is mainly because ABM facilitates an explicit representation of all three components pertinent to movement data-space, time, moving object [83].…”

Section: Movement Representation: An Agent-based Perspectivementioning

confidence: 99%

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

Rahimi

Moore

Whigham

2021

IJGI

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Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: ‘attribute,’ ‘actor,’ and ‘autonomous agent’. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3x3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: ‘association,’ ‘intervention,’ and ‘counterfactual’. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step.

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“…They are also increasingly being accepted as a powerful tool that allows for the explicit representation of the processes underlying large-scale animal movement patterns [29][30][31][32][33] . They are also popular in pedestrian modelling [34][35][36][37][38] , in studying the effects of movement behaviours on epidemic spread 39,40 , and in crowd movement simulation 41,42 .…”

Section: Proposing Agents For Movement Modellingmentioning

confidence: 99%

A Vector-agent Approach to (Spatiotemporal) Movement Modelling and Reasoning

Rahimi¹,

Moore²,

Whigham³

2021

Preprint

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Modelling a complex system of autonomous individuals moving through space and time essentially entails understanding the (heterogeneous) spatiotemporal context, interactions with other individuals, their internal states and making any underlying causal interrelationships explicit, a task for which agents (including vector-agents) are specifically well-suited. Building on a conceptual model of agent space-time and reasoning behaviour, a design guideline for an implemented vector-agent model is presented in this article as an example. The movement of football players was chosen as it is appropriately constrained in possible space, time and individual actions. Sensitivity-variability analysis was applied to measure the performance of different configurations of system components on the emergent movement patterns. The model output varied more when the condition of the contextual actors (players’ role-areas) were manipulated. In conclusion, ABMs can contribute to our understanding of movement and how causally-relevant evidence could be produced, through a proposed agent equipped with active causal knowledge.

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Towards Agent-Based Crowd Simulation in Airports Using Games Technology

Cited by 12 publications

References 9 publications

A perceptual study into the behaviour of autonomous agents within a virtual urban environment

A perceptual study into the behaviour of autonomous agents within a virtual urban environment

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

A Vector-agent Approach to (Spatiotemporal) Movement Modelling and Reasoning

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