The Proposal of a Concept of Artificial Situational Awareness in ATC

Automation is one of the most promising solutions to the airspace capacity problem. However, we believe that in order to safely implement advanced automation concepts in air traffic control, it is necessary for AI and humans to share situational awareness. One of the main objectives of this concept proposal is to explore the effects and possi-bilities of distributed human-machine situational awareness in en-route air traffic control operations. Instead of automating isolated individual tasks, such as conflict detection or coordination, we propose to create a basis for automation by developing an intelligent situation-aware system. The sharing of the same situational awareness be-tween the members of the air traffic controller team and AI enables the automated system to reach the same conclu-sions as air traffic controllers when faced with the same problem and to be able to explain the reasons for these conclusions. Machine learning can be used to predict, estimate and filter at the level of individual probabilistic events, an area in which it has shown great ability so far, whereas the reasoning engine can be used to represent knowledge and draw conclusions based on all the available data and explain the reasons for these conclusions. In this way, the artificial situational awareness system will pave the way for future advanced automation based on machine learning. Here, we will explore which technologies and concepts are useful in building the artificial situational awareness system and propose the methodology for testing the AI situational awareness

The Proposal of a Concept of Artificial Situational Awareness in ATC

Automation is one of the most promising solutions to the airspace capacity problem. However, we believe that in order to safely implement advanced automation concepts in air traffic control, it is necessary for AI and humans to share situational awareness. One of the main objectives of this concept proposal is to explore the effects and possi-bilities of distributed human-machine situational awareness in en-route air traffic control operations. Instead of automating isolated individual tasks, such as conflict detection or coordination, we propose to create a basis for automation by developing an intelligent situation-aware system. The sharing of the same situational awareness be-tween the members of the air traffic controller team and AI enables the automated system to reach the same conclu-sions as air traffic controllers when faced with the same problem and to be able to explain the reasons for these conclusions. Machine learning can be used to predict, estimate and filter at the level of individual probabilistic events, an area in which it has shown great ability so far, whereas the reasoning engine can be used to represent knowledge and draw conclusions based on all the available data and explain the reasons for these conclusions. In this way, the artificial situational awareness system will pave the way for future advanced automation based on machine learning. Here, we will explore which technologies and concepts are useful in building the artificial situational awareness system and propose the methodology for testing the AI situational awareness

Context-Aware Self-Healing for Small Cell Networks

These can be an invaluable source of information for the management of the network, in a way that we have denominated as context-aware SON, which is the approach proposed in this thesis. To develop this concept, the thesis follows a top-down approach. Firstly, the characteristics of the cellular deployments are assessed, especially for indoor small cell networks. In those scenarios, the need for context-aware SON is evaluated and considered indispensable. Secondly, a new cellular architecture is defined to integrate both context information and SON mechanisms in the management plane of the mobile network. Thus, the specifics of making context an integral part of cellular OAM/SON are defined. Also, the real-world implementation of the architecture is proposed. Thirdly, from the established general SON architecture, a logical self-healing framework is defined to support the context-aware healing mechanisms to be developed. Fourthly, different self-healing algorithms are defined depending on the failures to be managed and the conditions of the considered scenario. The mechanisms are based on probabilistic analysis, making use of both context and network data for detection and diagnosis of cellular issues. The conditions for the implementation of these methods are assessed. Their applicability is evaluated by means of simulators and testbed trials. The results show important improvements in performance and capabilities in comparison to previous methods, demonstrating the relevance of the proposed approach.The last years have seen a continuous increase in the use of mobile communications. To cope with the growing traffic, recently deployed technologies have deepened the adoption of small cells (low powered base stations) to serve areas with high demand or coverage issues, where macrocells can be both unsuccessful or inefficient. Also, new cellular and non-cellular technologies (e.g. WiFi) coexist with legacy ones, including also multiple deployment schemes (macrocell, small cells), in what is known as heterogeneous networks (HetNets). Due to the huge complexity of HetNets, their operation, administration and management (OAM) became increasingly difficult. To overcome this, the NGMN Alliance and the 3GPP defined the Self-Organizing Network (SON) paradigm, aiming to automate the OAM procedures to reduce their costs and increase the resulting performance. One key focus of SON is the self-healing of the network, covering the automatic detection of problems, the diagnosis of their causes, their compensation and their recovery. Until recently, SON mechanisms have been solely based on the analysis of alarms and performance indicators. However, on the one hand, this approach has become very limited given the complexity of the scenarios, and particularly in indoor cellular environments. Here, the deployment of small cells, their coexistence with multiple telecommunications systems and the nature of those environments (in terms of propagation, coverage overlapping, fast demand changes and users' mobility) introduce many challenges for classic SON. On the other hand, modern user equipment (e.g. smartphones), equipped with powerful processors, sensors and applications, generate a huge amount of context information. Context refers to those variables not directly associated with the telecommunication service, but with the terminals and their environment. This includes the user's position, applications, social data, etc

Responding to Urban Sphere’s Mobility Challenge: A Case of Nepal’s Historic City

Urbanization is a globally shared challenge and Nepal, a small developing country in the foot of the Himalayas, is no exception. For Lalitpur Metropolitan City (LMC), the country’s second ranked city in terms of population density, the situation is complex as numerous historic and artistic monuments including a UNESCO World Heritage property make structural adjustment prohibitive. As a practical and sustainable response to the mobility challenge, then, LMC has teamed up with the City’s stakeholders to transform into a cyclable city. Based on a case study that employed in-depth qualitative interviewing of specialized populations, this study attempted to validate City’s course of actions in light of global trend in the use of bicycles, and discussed the movement from the perspective of sustainable urban governance. The study found that the case city is on the right trajectory for tackling urbanization challenges with sustainable means, aided by the collective wit of the political, administrative, and citizens’ power. Their effort was validated by the initiative’s alignment with the global benchmarking on the use of bicycle for sustainable mobility. The study concluded that factors such as collaborative public service design and local government led spatial management are holding keys for sustainable urban governance

Natural language processing for aviation safety: Extracting knowledge from publicly-available loss of separation reports

Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps,authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board,authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports,other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future,authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies

Unlocking Sustainability with Visualizations: Driving the Driven through the Whys and Hows

Visualizations have been broadly employed to help individuals understand complex environmental issues and encourage sustainable behaviors. However, sustainability knowledge only sometimes transpires to actual green practices. In this study, we explain the effects of post-trip visualized storytelling on eco-driving behaviors. We conducted a laboratory experiment involving eye-tracking and driving simulation. This study contributes to the literature by unraveling the impact of visualized narratives on behaviors and demonstrating eco-driving behaviors in multiple manifestations

Natural language processing for aviation safety : Extracting knowledge from publicly-available loss of separation reports

Background: The air traffic management (ATM) system has historicallycoped with a global increase in traffic demand ultimately leading toincreased operational complexity.When dealing with the impact of this increasing complexity on systemsafety it is crucial to automatically analyse the losses of separation(LoSs) using tools able to extract meaningful and actionableinformation from safety reports.Current research in this field mainly exploits natural languageprocessing (NLP) to categorise the reports,with the limitations that theconsidered categories need to be manually annotated by experts andthat general taxonomies are seldom exploited.Methods: To address the current gaps,authors propose to performexploratory data analysis on safety reports combining state-of-the-arttechniques like topic modelling and clustering and then to develop analgorithm able to extract the Toolkit for ATM Occurrence Investigation(TOKAI) taxonomy factors from the free-text safety reports based onsyntactic analysis.TOKAI is a tool for investigation developed by EUROCONTROL and itstaxonomy is intended to become a standard and harmonisedapproach to future investigations.Results: Leveraging on the LoS events reported in the publicdatabases of the Comisi n de Estudio y An lisis de Notificaciones deIncidentes de Tr nsito A reo and the United Kingdom AirproxBoard,authors show how their proposal is able to automaticallyextract meaningful and actionable information from safetyreports,other than to classify their content according to the TOKAItaxonomy.The quality of the approach is also indirectly validated by checking theconnection between the identified factors and the main contributor ofthe incidents.Conclusions: Authors' results are a promising first step toward the fullautomation of a general analysis of LoS reports supported by resultson real-world data coming from two different sources.In the future,authors' proposal could be extended to othertaxonomies or tailored to identify factors to be included in the safetytaxonomies.KeywordsATM, Safety, Resilience, Natural Language Processing, Losses ofSeparation, Safety Reports, TOKA

How do principles for human-centred automation apply to Disruption Management Decision Support?

While automation of signal and route setting is routine, the use of automation or decision support in disruption management processes is far less common. Such support offers significant advantages in optimising re-planning of both timetable and resources (crew and rolling stock), and has value in offering a 'shared view' of re-planning across the many actors manage disruption. If this vision is to be realised, however, disruption management decision support and automation must adhere to proven principles for effective human-agent cooperation. This paper synthesises data from a programme of work to understand user requirements for automated disruption support tools. It then compares these outputs with two frameworks for human-centred automation - one general (Klein et al's [2004] ten challenges for automation) and one transport specific (Balfe et al’s [2012] principles for transport automation). Emergent design requirements include the need for iterative modification of rescheduling parameters throughout a disruption, visibility of the reasoning behind options, accountability remaining in the hands of disruption controllers, and the need for the automated disruption support tools to take a multi-dimensional view of disruption that varies depending on the event encountered. The paper reflects on the practical utility of high-level design principles for automated disruption support tools

How do principles for human-centred automation apply to Disruption Management Decision Support?

While automation of signal and route setting is routine, the use of automation or decision support in disruption management processes is far less common. Such support offers significant advantages in optimising re-planning of both timetable and resources (crew and rolling stock), and has value in offering a 'shared view' of re-planning across the many actors manage disruption. If this vision is to be realised, however, disruption management decision support and automation must adhere to proven principles for effective human-agent cooperation. This paper synthesises data from a programme of work to understand user requirements for automated disruption support tools. It then compares these outputs with two frameworks for human-centred automation - one general (Klein et al's [2004] ten challenges for automation) and one transport specific (Balfe et al’s [2012] principles for transport automation). Emergent design requirements include the need for iterative modification of rescheduling parameters throughout a disruption, visibility of the reasoning behind options, accountability remaining in the hands of disruption controllers, and the need for the automated disruption support tools to take a multi-dimensional view of disruption that varies depending on the event encountered. The paper reflects on the practical utility of high-level design principles for automated disruption support tools