1,720,952 research outputs found
Using Causal Discovery to Design Agent-based Models
No full textDesigning agent-based models is a difficult task. Some guidelines exist to aid modelers in designing their models, but they generally do not include specific details on how the behavior of agents can be defined. This paper therefore proposes the AbCDe methodology, which uses causal discovery algorithms to specify agent behavior. The methodology combines important expert insights with causal graphs generated by causal discovery algorithms based on real-world data. This causal graph represents the causal structure among agent-related variables, which is then translated to behavioral properties in the agent-based model. To demonstrate the AbCDe methodology, it is applied to a case study in the airport security domain. In this case study, we explore a new concept of operations, using a service lane, to improve the efficiency of the security checkpoint. Results show that the models generated with the AbCDe methodology have a closer resemblance with the validation data than a model defined by experts alone.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Air Transport & Operation
Modelling situation awareness relations in a multiagent system
Get PDFThere is broad consensus that situation awareness (SA) plays a key role in agent-based modelling of complex sociotechnical systems. However in the social sciences and human factors literature there are different views on what SA is and how it could be modelled. More specifically, one school of research considers SA as the process of gaining awareness, another school refers to it as to the product of gaining awareness, whereas the third school sees SA as a combination of the process and product. Typically, agent-based modelling of SA is done from the second view for each individual agent, possibly with additional social components to enable interaction. Current developments in multiagent systems indicate that social abilities and relations between agents should be not an addition, but at the core of any model of a sociotechnical system. To address this issue, we develop a mathematical modelling framework of SA relations between agents which supports all three views. The use of the framework is demonstrated by an example of retrospective accident modelling from the aviation domain.Control & OperationsAerospace Engineerin
Analysing Vessel Behaviour for Medium-Term Prediction of Vessel Collision Risk
No full textMaritime traffic has to deal with the risk of collision on a daily basis. Currently, Vessel Traffic Services Operators are provided with short-term prediction methods, used to resolve potential collisions. Research is done to predict collision risk at a larger time horizon, which is expected to provide a Vessel Traffic Services Operator (VTSO) with more time and information to anticipate upon and prevent situations with high risk of collision from developing. This thesis focusses on forming the basis for the prediction component of this goal. The objective is to provide a basic understanding of the process of medium-term behaviour of vessels. This is done by performing a data analysis of a case study of the vessel traffic off the coast of Rotterdam, investigating which variables can be used to predict the intent of a vessel. Two aspects of the intent of a vessel are considered: where the vessel intends to end (within the scope of the scene), and which intermediate waypoints it plans to follow. Entry points, exit points and waypoints are clustered using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique. Waypoints are derived by detecting change-points in the course of vessels using binary segmentation. Variables from the dataset are selected and it is investigated which variables can distinguish between different intents, given the entry point of the vessel. The results are that the variables 'course' and 'destination' can distinguish between routes sufficiently enough to investigate them further. This further investigation for the course variable is due to its dependence on vessel position, among other variables. Other variables may add value also, but then in combination with these two variables. The waypoint determination has not yet been successfully implemented, but it is regarded as a promising means to describe and predict vessel intent in more detail, partially due to the conclusions drawn regarding the course variable.Aerospace EngineeringControl & OperationsControl and OperationsAE531
Agent-based Approach to Retrospective Analysis of Aviation Accidents
No full textAgent based modelling is a relatively new paradigm allowing to model the various components of the complex socio-technical air transportation system as well as the interactions between these components. Because situation awareness plays a key role in agent-based modelling, researchers at NLR had developed approaches in modelling Multi-Agent Situation Awareness (MASA) and MASA differences, including methods for their use in prospective safety risk analysis, i.e. to predict safety risk well ahead of an accident. The aim of the current research was to investigate how the combination of MASA and agent-based modelling can be used for retrospective safety analysis, i.e. to conduct an in-hindsight analysis of an accident occurrence. More specifically, the objective of this research was: To create a structured approach for retrospective modelling and analysis of an air transportation accident through a combined use of Multi-Agent Situation Awareness and agent-based modelling. The research showed that contributing factors to an accident can often be specified as MASA differences. This finding formed the basis for the development of a formal agent based approach to retrospective accident analysis. The working of this formal agent-based retrospective analysis approach has subsequently been demonstrated for three specific accidents from aviation history. Though information gathered from the occurrence database Skybrary, all relevant agents have been identified and their states and perceived states have been modelled at points where MASA differences are likely to occur. Finally the causation and propagation of MASA differences in the agent-based model are analysed. Based on the insights obtained from these analyses, it is concluded that a MASA and agent-based retrospective analysis of accidents yields new insights that are not obtained using a classical retrospective safety analysis.ATOAerospace Engineerin
Design of a Demand Responsive Transport service using Distributed Constraint Optimization for airport access
No full textAccessibility is one of the key performance indicators in the evaluation of a multimodal transport system and, as a result, transport planning has become increasingly more oriented towards it. Demand Responsive Transport (DRT) services have been proposed as a measure for increasing accessibility of a Public Transit (PT) network by servicing users in inaccessible areas. Through multimodal planning and coordination, a DRT service can be integrated within the extended PT network and supply the network optimally. In the context of PT users headed toward airports, an integrated DRT service is proposed for those with extended first-mile connections. This service makes use of taxis to transport users to transit points of a dedicated train line supplying a major European airport. Ride-sharing is considered, while optimal order of service and transit points for modal change are determined. To capture the decentralized nature of matching taxis to users, a multi-agent-based algorithm based on Distributed Constraint optimization Problems (DCOPs) is developed. Real-time information about routes and fixed schedules of the PT network are extracted via a dedicated routing Application Programming Interface (API). Experiments validate the applicability of the proposed solution by reporting a decrease in users’ first-mile travel time that is approximately analogous to the modal share the service captures.Air Transport & Operation
Causes, Identification and Repair of loss of Common Ground in coordination in ATM (Air Traffic Management)
No full textOver a century has passed since humans took to commercial flying. Traditional safety practices have worked well but the last decade has seen the need for an updated understanding of ATM safety. The modern safety views are complementary to the traditional ones but are also a new way of understanding and enabling safety practices. This master thesis report presents a comprehensive review of the sources chosen from literature to better understand how a complex sociotechnical system, such as ATM, would operate. Certain selected coordination aspects will be the focus of this master thesis and will be used to model and analyse an ATM case. The ultimate aim of this research project is to add to the growing body of knowledge in the field of ATM safety, to make flying increasingly safer and to enable a complex system to be resilient.Aerospace EngineeringAir Transport Operation
A Machine Learning Approach to Flight Safety Event Prediction
No full textSafety occurrences in the aviation industry are nowadays commonly regarded as the outcome of a complex system. Due to this systemic view on safety airlines pursue to understand this complex, underlying system and aim to proactively act upon the occurrence of these events. The most prevalent implementation of flight safety event detection is however still threshold analysis, which has no such implications. On the other hand, Machine Learning methods have readily proven to be an efficient and valuable solutions in predicting the occurrence of anomalies in data, such as flight safety events. However, existing methods search for anomalies in datasets encompassing the anomaly, i.e. direct datasets. On the contrary, this study approached airline operations as a complex system which the outcome could be the occurrence of a flight safety event. Hence, the question was raised whether a set of indirect precursors could be significant in predicting flight safety events. That is why common airline processes were selected, in consultation with industry experts, and their indirect data considered. The aim of this study was to evaluate this concept by evaluating a set of precursors for a particular flight safety event (a case study). The Knowledge Discovery in Databases framework was the general guideline throughout this research, with a Relief and Neural Network algorithm as transformation and data mining step respectively. This study showed that the considered processes were significant in predicting the occurrence of a safety event, although the found precursors could not fully encompass the event under investigation. The classification performance of the methodology was characterised by a large number of false positives, which originated from the problem's class skewness. The Matthews Correlation Coefficient proved to be a well-balanced optimisation objective for such problems and overcame this drift. Locally, the weight optimisation showed a set of confidently classified false positives and negatives confined further improvement. These misclassifications were found to be the result of a lack of adequate information. Nevertheless, the considered information did display to be significant as the obtained Matthews Correlation Coefficient and recall underpinned, particularly in the light of the class imbalance and the anomalous nature of flight safety events.Aerospace Engineerin
Formal modelling and verification of a multi-agent negotiation approach for airline operations control
No full textThis paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.Interactive IntelligenceAir Transport & Operation
The Effect of Relationship Biases on AOCC Performance
No full textPassengers are frequently affected by airline disruptions, leading to a poorer than expected passenger experience. Airlines are affected by disruptions in the order of billions of dollars. Managing disruptions effectively is therefore paramount for an airline’s long-term commercial success. In spite of decision-support tools being introduced to facilitate Airline Operational Control Center (AOCC) decision-making, their adoption rate is low. For the foreseeable future, humans will unquestionably remain in the loop when it comes to AOCCdisruption management and human-factors will continue to come into play in AOCC decision-making. Toimprove AOCC decision-making, the effects of human factors on decision-making must be well understood. Bias is a human factor that affects decision-making and a relationship bias is a bias where previous negative experiences, between two individuals, will negatively affect future interactions they may have. A lack of trust, unwillingness to concede (in negotiations) or even a reluctance to interact, are a few examples on how a relationship bias may operationally manifest itself. AOCC decision-makers collaborate with one another to arrive at a integrated solution that mitigates an airline’s disruption. If a relationship biases exist within the AOCC, this negatively affects collaboration among AOCC decision-makers the development of solutions. The effect of the relationship bias on the solutions selected to mitigate an airline’s disruptions motivates the study of the effect of the relationship bias on AOCC performance. The fact that the relationship bias on AOCC decision-making has never been research, further motivates its study. We hope to address this research gap by evaluating the effects of the relationship bias on AOCC performance. More precisely, the research objective is to evaluate the effects of the relationship bias on AOCC performance, by modelling AOCC decision-making through a Naturalistic Decision-making framework using Klein’s Extended Recognition-Primed Decision model, and modelling AOCC social decision-making and interactionsusing Chow’s Co-Ladder model. The methodology involves formalizing AOCC goals through a framework [Popova and Sharpanskykh, 2008] which enables us to measure organizational performance. Furthermore, it involves formally integrating Bruce’s extension [Bruce, 2011a] of Klein’s extended Recognition-primed Decision (RPD) model with Chow’s social interaction model [Chow et al., 2000]. The model is finally simulated for a scenario where a scheduled flight suffers a mechanical disruption and the performance is evaluated based on a goal satisfaction and operational costs. There are three main contributions of this research. Firstly, the individual cognition model developed makes it possible to model AOCC decision-maker’s individual cognition within the complex and dynamic AOCC environment. The second contribution is the proposed integrated model, which make it possible to integrate agent individual cognition and agent social decision-making and interactions. The third contribution is the evaluation of various possible relationship biases, which makes it possible evaluate the effect of different relationship types on AOCC performance. The research conducted led to a few interesting findings. For example, only some relationship biases lead to a significant decreased in AOCC performance, whereas other relationships have a negligible effect. Another interesting finding was that if there is a relationship bais between two agent, they are both not equally affected. The relationship bias only affects the AOCC control agent who requires information from a counterpart in order to develop their partial solution.Aerospace Engineerin
A Multi-Agent Negotiation Approach to Formation Flying Coordination for Civil Aviation
No full textThis study proposes a novel coordination approach for the purpose of formation flying in civil aviation. Inspired by the natural phenomenon of bird flight, formation flying has proven to reduce the aerodynamic drag of trailing aircraft that follow the wake of a leader. We investigate the network scale fuel-savings potential of this operational concept, which could be implemented in the existing global airline fleet. In order to realise the benefits of formation flying, a concept of operations is required that accounts for the strategic nature and interests of flight operators. We propose a concept of operations that coordinates formation flying for flight operators who are strategic, intelligent, and social. A multi-agent negotiation coordination mechanism is developed and evaluated, which enables competitive flight-agents to maximize their utility by engaging in flight formations. This paper presents an adjusted Contract Net Protocol that governs formation task and role allocation, where the agents reason through heuristic strategies and learning capabilities. We show that self-interested formation managers are likely to coordinate towards socially desirable outcomes. No preferable bidding strategies have been found, as they are shown to depend on non-local qualities. The proposed concept of operations achieves a 5.8\% fuel-flow reduction for a large-scale transatlantic flight network.Aerospace Engineerin
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