Dissertations / Theses on the topic 'Air traffic data'

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 209-219).
The Air Traffic Management (ATM) system enables air transportation by ensuring a safe and orderly air traffic flow. As the air transport demand has grown, ATM has become increasingly challenging, resulting in high levels of congestion, flight delays and environmental impacts. To sustain the industry growth foreseen and enable more efficient air travel, it is important to develop mechanisms for better understanding and predicting the air traffic flow behavior and performance in order to assist human decision-makers to deliver improved airspace design and traffic management solutions. This thesis presents a data-driven approach to modeling air traffic flows and analyzes its contribution to supporting system level ATM decision-making. A data analytics framework is proposed for high-fidelity characterization of air traffic flows from large-scale flight tracking data. The framework incorporates a multi-layer clustering analysis to extract spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a detailed characterization of terminal area traffic flows in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the metroplexes analyzed and highlight varied action areas to improve air traffic operations at these systems. Finally, the knowledge derived from flight trajectory data analytics is leveraged to develop predictive and prescriptive models for metroplex configuration and capacity planning decision support. Supervised learning methods are used to create prediction models capable of translating weather forecasts into probabilistic forecasts of the metroplex traffic flow structure and airport capacity for strategic time horizons. To process these capacity forecasts and assist the design of traffic flow management strategies, a new optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal Airport Acceptance Rates in Ground Delay Programs, an overall delay reduction of up to 9.7% is achieved.
by Mayara Condé Rocha Murça.
Ph. D.

Air traffic demand is growing. New methods of airspace design are required that can enable new designs, do not depend on current operations, and can also support quantifiable performance goals. The main goal of this thesis is to develop methods to model inherent safety and control cost so that these can be included as principal objectives of airspace design, in support of prior work which examines capacity. The first contribution of the thesis is to demonstrate two applications of airspace analysis and design: assessing the inherent safety and control cost of the airspace. Two results are shown, a model which estimates control cost depending on autonomy allocation and traffic volume, and the characterization of inherent safety conditions which prevent unsafe trajectories. The effects of autonomy ratio and traffic volume on control cost emerge from a Monte Carlo simulation of air traffic in an airspace sector. A maximum likelihood estimation identifies the Poisson process to be the best stochastic model for control cost. Recommendations are made to support control-cost-centered airspace design. A novel method to reliably generate collision avoidance advisories, in piloted simulations, by the widely-used Traffic Alert and Collision Avoidance System (TCAS) is used to construct unsafe trajectory clusters. Results show that the inherent safety of routes can be characterized, determined, and predicted by relatively simple convex polyhedra (albeit multi-dimensional and involving spatial and kinematic information). Results also provide direct trade-off relations between spatial and kinematic constraints on route geometries that preserve safety. Accounting for these clusters thus supports safety-centered airspace design. The second contribution of the thesis is a general methodology that generalizes unifying principles from these two demonstrations. The proposed methodology has three steps: aggregate data, synthesize lean model, and guide design. The use of lean models is a result of a natural flowdown from the airspace view to the requirements. The scope of the lean model is situated at a level of granularity that identifies the macroscopic effects of operational changes on the strategic level. The lean model technique maps low-level changes to high-level properties and provides predictive results. The use of lean models allows the mapping of design variables (route geometry, autonomy allocation) to design evaluation metrics (inherent safety, control cost).

Air Traffic Management (ATM) aims at ensuring safe and efficient movement of aircraft in the airspace. The National Airspace System is currently undergoing a comprehensive overhaul known as NextGen. With the predicted growth of air transportation, providing traffic flow managers with the tools to support decision making is essential. These tools should aid in accommodating the air traffic throughput increase, while limiting controller workload and ensuring high safety levels. In the National Airspace System (NAS), the goal of en-route Traffic Flow Management (TFM) is to balance air traffic demand against available airspace capacity, in order to ensure a safe and expeditious flow of aircraft, both under nominal and perturbed conditions. The objective of this thesis is to develop a better understanding of how to analyze, model and simulate air traffic in a given airspace, under both nominal and degraded conditions. First, a new framework for en-route Traffic Flow Management and Airspace Health Monitoring is developed. It is based on a data-driven approach for air traffic flow modeling using historical data. This large-scale 3D flow network of the Cleveland center airspace provides valuable insight on airspace complexity. A linear formulation for optimizing en-route Air Traffic is proposed. It takes into account a controller taskload model based on flow geometry, in order to estimate airspace capacity. The simulations run demonstrate the importance of sector constraints and traffic demand patterns in estimating the throughput of an airspace. To analyze airspace degradation, weather blockage maps based on vertically integrated liquid (VIL) are incorporated in the model, representing weather perturbations on the same data set used to compute the flows. Comparing the weather blockages and the network model of the airspace provides means of quantifying airspace degradation. Simulations under perturbed conditions are then run according to different objectives. The results of the simulations are compared with the data from these specific days, to identify the advantages and drawbacks of the present model.

The scope of this study was to evaluate the impact of the air traffic controller-to-pilot communication standard known as CPDLC or Data-Communication on the future air traffic operations. The impact was evaluated from the double viewpoint of airport delays and air traffic controllersâ workload. RAMS simulation software is used to perform all the runs and from its output data the values of terminal area delays and controllers workload are obtained. The New York Metroplex terminal area was used as a case study. Because of its complexity, where three major airports (i.e. JFK, Newark, and La Guardia) interact and constraint each other, this area was particularly interesting to be studied and the data analyzed gave a valuable insight on the possible future impact of Data-Communication in congested terminal areas. The results of the study, based on some previous man-in-the-loop simulations performed by the FAA in the nineties, showed that significant potential benefits could be obtained with the complete implementation of such technologies in the workload experienced by air traffic controllers. Moreover some small but not negligible benefits were obtained in the total delays accrued by each airport studied. On the other hand, the simulations of the future demand predicted by the FAA demonstrated that without a significant increment in capacity or limitation on the traffic growth intolerable delays would be recorded across the NAS in the future. For the complexity of the simulation model calibration and for the very time-consuming run time not all the scenarios described in the methodology were tested, demonstrating the weakness of RAMS as a ground simulation model.
Master of Science

Air Traffic Management (ATM) is at an exciting frontier. The volume of air traffic is reaching the safe limits of current infrastructure. Yet, demand for more air traffic continues. To meet capacity demands, ATM data networks are increasing in complexity with: greater infrastructure integration, higher availability and precision of services; and the introduction of unmanned systems. Official recommendations into previous disruptive outages have high-lighted the need for operators to have richer monitoring capabilities and operational systems visibility, on-demand, in response to challenges. The work presented in this thesis, helps ATM operators better understand and increase visibility into the behaviour of their services and infrastructure, with the primary aim to inform decision-making to reduce service disruption. This is achieved by combining a container-based NFV framework with Software- Defined Networking (SDN). The application of SDN+NFV in this work allows lightweight, chain-able monitoring and anomaly detection functions to be deployed on-demand, and the appropriate (sub)set of network traffic routed through these virtual network functions to provide timely, context-specific information. This container-based function deployment architecture, allows for punctual in-network processing through the instantiation of custom functionality, at appropriate locations. When accidents do occur, such as the crash of a UAV, the lessons learnt should be integrated into future systems. For one such incident, the accident investigation identified a telemetry precursor an hour prior. The function deployment architecture allows operators to extend and adapt their network infrastructure, to incorporate the latest monitoring recommendations. Furthermore, this work has examined relationships in application-level information and network layer data representing individual examples of a wide range of generalisable cases including: between the cyber and physical components of surveillance data, the rate of change in telemetry to determine abnormal aircraft surface movements, and the emerging behaviour of network flooding. Each of these examples provide valuable context-specific benefits to operators and a generalised basis from which further tools can be developed to enhance their understanding of their networks.

La circulation routière est une source majeure de pollution atmosphérique dans les zones urbaines. Les décideurs insistent pour qu’on leur propose de nouvelles solutions, y compris de nouvelles stratégies de management qui pourraient directement faire baisser les émissions de polluants. Pour évaluer les performances de ces stratégies, le calcul des émissions de pollution devrait tenir compte de la dynamique spatiale et temporelle du trafic. L’utilisation de capteurs traditionnels sur route (par exemple, capteurs inductifs ou boucles de comptage) pour collecter des données en temps réel est nécessaire mais pas suffisante en raison de leur coût de mise en oeuvre très élevé. Le fait que de telles technologies, pour des raisons pratiques, ne fournissent que des informations locales est un inconvénient. Certaines méthodes devraient ensuite être appliquées pour étendre cette information locale à une grande échelle. Ces méthodes souffrent actuellement des limites suivantes : (i) la relation entre les données manquantes et la précision de l’estimation ne peut être facilement déterminée et (ii) les calculs à grande échelle sont énormément coûteux, principalement lorsque les phénomènes de congestion sont considérés. Compte tenu d’une simulation microscopique du trafic couplée à un modèle d’émission, une approche innovante de ce problème est mise en oeuvre. Elle consiste à appliquer des techniques de sélection statistique qui permettent d’identifier les emplacements les plus pertinents pour estimer les émissions des véhicules du réseau à différentes échelles spatiales et temporelles. Ce travail explore l’utilisation de méthodes statistiques intelligentes et naïves, comme outil pour sélectionner l’information la plus pertinente sur le trafic et les émissions sur un réseau afin de déterminer les valeurs totales à plusieurs échelles. Ce travail met également en évidence quelques précautions à prendre en compte quand on calcul les émissions à large échelle à partir des données trafic et d’un modèle d’émission. L’utilisation des facteurs d’émission COPERT IV à différentes échelles spatio-temporelles induit un biais en fonction des conditions de circulation par rapport à l’échelle d’origine (cycles de conduite). Ce biais observé sur nos simulations a été quantifié en fonction des indicateurs de trafic (vitesse moyenne). Il a également été démontré qu’il avait une double origine : la convexité des fonctions d’émission et la covariance des variables de trafic
Road traffic is a major source of air pollution in urban areas. Policy makers are pushing for different solutions including new traffic management strategies that can directly lower pollutants emissions. To assess the performances of such strategies, the calculation of pollution emission should consider spatial and temporal dynamic of the traffic. The use of traditional on-road sensors (e.g. inductive sensors) for collecting real-time data is necessary but not sufficient because of their expensive cost of implementation. It is also a disadvantage that such technologies, for practical reasons, only provide local information. Some methods should then be applied to expand this local information to large spatial extent. These methods currently suffer from the following limitations: (i) the relationship between missing data and the estimation accuracy, both cannot be easily determined and (ii) the calculations on large area is computationally expensive in particular when time evolution is considered. Given a dynamic traffic simulation coupled with an emission model, a novel approach to this problem is taken by applying selection techniques that can identify the most relevant locations to estimate the network vehicle emissions in various spatial and temporal scales. This work explores the use of different statistical methods both naïve and smart, as tools for selecting the most relevant traffic and emission information on a network to determine the total values at any scale. This work also highlights some cautions when such traffic-emission coupled method is used to quantify emissions due the traffic. Using the COPERT IV emission functions at various spatial-temporal scales induces a bias depending on traffic conditions, in comparison to the original scale (driving cycles). This bias observed in our simulations, has been quantified in function of traffic indicators (mean speed). It also has been demonstrated to have a double origin: the emission functions’ convexity and the traffic variables covariance

Urban arterial corridors are landscapes that give rise to short and long-term exposures to transportation-related pollution. With high traffic volumes, congestion, and a wide mix of road users and land uses at the road edge, urban arterial environments are important targets for improved exposure assessment to traffic-related pollution. Applying transportation management strategies to reduce emissions along arterial corridors could be enhanced if the ability to quantify and evaluate such actions was improved. However, arterial roadsides are under-sampled in terms of air pollution measurements in the United States and using observational data to assess such effects has many challenges such as lack of control sites for comparisons and temporal autocorrelation. The availability of traffic-related data is also typically limited in air monitoring and health studies. The work presented here uses unique long-term roadside air quality monitoring collected at the intersection of an urban arterial in Portland, OR to characterize the roadside atmospheric environment. This air quality dataset is then integrated with traffic-related data to assess various methods for improving exposure assessment and the roadside environment. Roadside nitric oxide (NO), nitrogen dioxide (NO2), and particle number concentration (PNC) measurements all demonstrated a relationship with local traffic volumes. Seasonal and diurnal characterizations show that roadside PM2.5 (mass) measurements do not have a relationship with local traffic volumes, providing evidence that PM2.5 mass is more tied to regional sources and meteorological conditions. The relationship of roadside NO and NO2 with traffic volumes was assessed over short and long-term aggregations to assess the reliability of a commonly employed method of using traffic volumes as a proxy for traffic-related exposure. This method was shown to be insufficient for shorter-time scales. Comparisons with annual aggregations validate the use of traffic volumes to estimate annual exposure concentrations, demonstrating this method can capture chronic but not acute exposure. As epidemiology and exposure assessment aims to target health impacts and pollutant levels encountered by pedestrians, cyclists, and those waiting for transit, these results show when traffic volumes alone can be a reliable proxy for exposure and when this approach is not warranted. Next, it is demonstrated that a change in traffic flow and change in emissions can be measured through roadside pollutant concentrations suggesting roadside pollution can be affected by traffic signal timing. The effect of a reduced maximum traffic signal cycle length on measurements of degree of saturation (DS), NO, and NO2 were evaluated for the peak traffic periods in two case studies at the study intersection. In order to reduce bias from covariates and assess the effect due to the change in cycle length only, a matched sampling method based on propensity scores was used to compare treatment periods (reduced cycle length) with control periods (no change in cycle length). Significant increases in DS values of 2-8% were found along with significant increases of 5-8ppb NO and 4-5ppb NO2 across three peak periods in both case studies. Without matched sampling to address the challenges of observational data, the small DS and NOx changes for the study intersection would have been masked and matched sampling is shown to be a helpful tool for future urban air quality empirical investigations. Dispersion modeling evaluations showed the California Line Source Dispersion Model with Queuing and Hotspot Calculations (CAL3QHCR), an approved regulatory model to assess the impacts of transportation projects on PM2.5, performed both poor and well when predictions were compared with PM2.5 observations depending on season. Varying levels of detail in emissions, traffic signal, and traffic volume data for model inputs, assessed using three model scenarios, did not affect model performance for the study intersection. Model performance is heavily dependent on background concentrations and meteorology. It was also demonstrated that CAL3QHC can be used in combination with roadside PNC measurements to back calculate PNC emission factors for a mixed fleet and major arterial roadway in the U.S. The integration of roadside air quality and traffic-related data made it possible to perform unique empirical evaluations of exposure assessment methods and dispersion modeling methods for roadside environments. This data integration was used for the assessment of the relationship between roadside pollutants and a change in a traffic signal setting, a commonly employed method for transportation management and emissions mitigation, but rarely evaluated outside of simulation and emissions modeling. Results and methods derived from this work are being used to implement a second roadside air quality station, to design a city-wide integrated network of air quality, meteorological, and traffic data including additional spatially resolved measurements with feedback loops for improved data quality and data usefulness. Results and methods are also being used to design future evaluations of transportation projects such as freight priority signaling, improved transit signal priority, and to understand the air quality impacts of changes in fleet composition such as an increase in electric vehicles.

The air traffic system in the United States is currently undergoing a complete overhaul known as "NextGen". NextGen is the FAA's initiative to update the antiquated National Airspace System (NAS) both procedurally and technologically to reduce costs to the users and negative impacts on the general public. There are currently numerous studies being conducted that are focused on finding optimal solutions to the problems of congestion, delay, and the high fuel and noise footprints associated aircraft operations. These studies require accurate simulation techniques to assess the potential benefits and drawbacks for new procedures and technology. One common method uses air traffic control radar data. As an aircraft travels through the air traffic control system, its latitude, longitude, and altitude are recorded at set intervals. From these values, estimates of groundspeed and heading can be derived. Researchers then use this data to estimate aircraft performance parameters such as engine thrust and aircraft configuration, variables essential to estimate fuel burn, noise, and emissions. This thesis creates a more accurate method of simulating aircraft performance based solely on air traffic control radar data during the arrival process. This tool will allow the benefits of different arrival procedures to be compared at a variety of airports and wind conditions before costly flight testing is required. The accuracy of the performance estimates will be increased using the Tool for Assessing Separation and Throughput (TASAT), a fast-time Monte Carlo aircraft simulator that can simulate multiple arrivals with a mixture of different aircraft types. The tool has succeeded in matching various recorded radar profiles and has produced fuel burn estimates with an RMS error of less than 200 pounds from top of descent to landing when compared to high fidelity operational data. The output from TASAT can also be ported to FAA software tools to make higher quality predictions of aircraft noise and emissions.

Through application of modern technology, aviation systems are becoming more automated and are relying less on antiquated air traffic control (ATC) voice systems. Aircraft are now able to wirelessly broadcast and receive identity and location information using transponder technology. This helps reduce controller workload and allows the aircraft to take more responsibility for maintaining safe separation. However, these systems lack source authentication methods or the ability to check the integrity of message content. This opens the door for hackers to potentially create fraudulent messages or manipulate message content.

This thesis presents a solution to handling many of the potential security issues in aircraft data communication. This is accomplished through the implementation of a Dual Path PKI (DPP) design which includes a novel approach to handling certificate revocation through session certificates. DPP defines two authentication protocols, one between aircraft and another between aircraft and ATC, to achieve source authentication. Digital signature technology is utilized to achieve message content and source integrity as well as enable bootstrapping DPP into current ATC systems. DPP employs cutting-edge elliptic curve cryptography (ECC) algorithms to increase performance and reduce overhead.

It is found that the DPP design successfully mitigates several of the cyber security concerns in aircraft and ATC data communications. An implementation of the design shows that anticipated ATC systems can accommodate the additional processing power and bandwidth required by DPP to successfully achieve system integrity and security.
Master of Science

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1999.
Includes bibliographical references (p. 125-130).
In an effort to reduce saturation in voice radio channels and to take advantage of space-based communication technologies in a cost-effective basis, data link communication between the flight deck and air traffic control (CPDLC) is gradually coming to the fore. Currently, there are three main flight deck interface designs for CPDLC, and a comparative human factors study of these designs is documented in this thesis. However, in spite of the recent development, there is little coherent understanding on the influence of hardware interface components on performance. To contribute to this understanding, the performance of two flight deck CPDLC interface designs were compared at the Boeing Company, and the result was used to estimate the performance of a third interface design. As a follow-on study, an experiment was conducted to examine the relative performance of four simplified interface configurations for CPDLC. The experiment found that there was little difference in performance (task processing time, accuracy and efficiency) among the four interface configurations in simple communication tasks. However, as the level of difficulty of these tasks increases, a dual interface configuration with separate functionality on each interface required the least amount of time to accomplish the stated tasks. The additional maneuverability provided by a dual-interface configuration with identical functionality on each interface did not appear to lead to significant additional performance gains compared with the dual interface configuration with separate functionality. In general, the single-interface configurations required longer processing times for complicated tasks and were also found to incur higher workload according to the NASA Task Load Index.
by Terence Ping Ching Fan.
S.M.

This paper formally analyzes the role of yearly snowfall in explaining the changes in home valuations near ski resorts in the western United States. Using data on housing values for selected U.S. Census tracts, combined with detailed weather data, passenger arrival data from 10% of all commercial air traffic, and characteristics of nearby ski resorts, I find precise and consistent estimates of days of snowfall and number of available destinations by airport on housing values.

How do air traffic controllers, or ATCos, distribute visual attention and can it vary between controllers? In this study, using primarily eye-tracking data and a couple of on-site interviews, these questions are explored. Two ATCos, with the most similar landings, had their eye-movements recorded with Tobii pro glasses 2 and further analysed by categorizing every fixation into different areas of interest during four landings. Two more ATCos were interviewed briefly during an observational visit to the control tower. The results showed that the ATCos distributed their attention fairly equally between the outside of the control tower and the inside. When attending to something outside the runway was the focus and when attention was inside the control tower the radar was usually the focus. The ATCos differed in their attention distribution by the presumably more experienced ATCo distributing their attention more outside the control tower than the presumably less experienced ATCo.  A large number of fixations were not categorized bringing the method of dividing the ATCos eye-tracking view into areas of interest into question.

Airplanes have different ways to connect to the ground, including satellite air-to-ground communication (SA2GC) and direct air-to-ground communication (DA2GC). Each connection/link offers a different varying amount of transmission capacity over flight time. The traffic generated in the airplane must be forwarded/sent to ground over the available links. It is however not clear how the traffic should be forwarded so that traffic quality of service (QoS) requirements are met. The thesis at hand considers this question, and implements an algorithm handling the forwarding decision with three different forwarding schemes. Those consider traffic parameters in calculating a value assigned to each traffic flow, over a combination of priority, delay requirement and the number of times a traffic flow is dropped. The forwarding algorithm relies on proposed in-flight broadband connectivity (IFBC) network traffic and air-to-ground (A2G) link models, which aim at approximating the network environment of future IFBC networks. It is shown that QoS requirements of traffic flows in terms of packet loss and delay cannot be satisfied with capacities offered by current DA2GC and SA2GC technology. For a future scenario, with higher assumed link capacities, the QoS requirements are met to a higher extent. This is shown in lower packet loss and delay experienced by the respective traffic flows. Further, it is shown that the performance can be improved with specific forwarding schemes used by the forwarding algorithm. It is also investigated how a web cache can be used as a fallback technology. For this a required web cache hit rate is found, which should be high enough to offload the network with content served from the cache. Overall, the thesis aims at proposing an efficient traffic forwarding technique, and at giving insight into an alternative if this technique fails.
Flygplan har olika sätt att ansluta till marken, inklusive satellit-mark-kommunikation (SA2GC) och direkt luft till markkommunikation (DA2GC). Varje anslutning/länk erbjuder en annan varierande mängd överföringskapacitet under flygtid. Den trafik som genereras i flygplanet måste vidarebefordras/skickas till marken över de tillgängliga länkarna. Det är emellertid inte klart hur trafiken ska vidarebefordras så att trafiksäkerhetskvaliteten (QoS) uppfylls. Avhandlingen handlar om denna fråga och implementerar en algoritm som hanterar vidarebefordringsbeslutet med tre olika vidarebefordringssystem. De betraktar trafikparametrar vid beräkning av ett värde som tilldelas varje trafikflöde, över en kombination av prioritet, fördröjningskrav och antalet gånger ett trafikflöde tappas. Vidarebefordringsalgoritmen är beroende av föreslagna bredbandsförbindelser (IFBC) i nätverk och A2G-länkmodeller, som syftar till att approximera nätverksmiljön för framtida IFBC-nätverk. Det visas att QoS-krav på trafikflöden när det gäller paketförlust och fördröjning inte kan tillgodoses med kapacitet som erbjuds av nuvarande DA2GC- och SA2GC-teknik. För ett framtida scenario, med högre antagna länkkapacitet, uppfylls QoS-kraven i högre utsträckning. Detta visas med lägre paketförlust och fördröjning som upplevs av respektive trafikflöden. Vidare är det visat att prestanda kan förbättras med specifika vidarekopplingsscheman som används av vidarebefordringsalgoritmen. Det undersöks också hur en webbcache kan användas som en återgångsteknik. För detta hittas en obligatorisk webbcache-träfffrekvens, som bör vara tillräckligt hög för att ladda upp nätverket med innehåll som serveras från cacheminnet. Sammanfattningsvis syftar uppsatsen till att föreslå en effektiv trafiköverföringsteknik och att ge insikt om ett alternativ om denna teknik misslyckas.

The purpose of this study is to explore the use of integrated probe vehicle, traffic and land use data to identify and characterize fine particulate matter (PM[subscript 2.5]) hot spot locations on urban arterial corridors. In addition, a preliminary analysis is conducted to consider volatile organic compound (VOC) hot spot locations. A pollutant hot spot is defined as a location on a corridor in which the mean pollutant concentrations are consistently above the 85th percentile of pollutant concentrations when compared to all locations along the corridor. In order to collect data for this study, an electric vehicle was equipped with instruments designed to measure PM[subscript 2.5] and total VOC (TVOC) concentrations. Second-by-second measurements were performed for each pollutant from both the right and left sides of the vehicle. Detailed meteorological, traffic and land use data is also available for this research. The results of a statistical analysis, including multiple regression, are used to better understand which data sources are most valuable in estimating PM[subscript 2.5] hot spot locations consistent with empirical data; knowledge is gained as to which variables have the strongest statistical relationships with traffic emissions and pollutant levels at a corridor level. A preliminary analysis is also completed to consider which variables are statistically related to TVOC hot spot locations. This research highlights the importance of considering both consistency and magnitude of pollutant concentrations when identifying hot spot locations. An objective of this research is to develop a method to identify urban arterial hot spot locations that provides a balance of efficiency (in terms of capital expenses, time, resources, expertise requirements, etc.) and accuracy.

A modeling framework to evaluate the costs and benefits of implementation of Con-troller Pilot Data Link Communication (CPDLC), and Air Traffic Management (ATM) decision support tools is proposed in this paper. The benefit/cost evaluation is carried out for four key alternatives namely alternative A: Do nothing scenario (only voice channel), alternative B: Voice channel supplemented with CPDLC, alternative C: Alternative B with ATM tools in a non-integrated scenario and finally alternative D: Alternative B with ATM tools in an integrated scenario. It is a bi-level model that cap-tures the linkages between various technologies at a lower microscopic level using a daily microscopic model (DATSIM) and transfers the measures of effectives to a higher macroscopic level. DATSIM stands for Data Link and Air Traffic Technologies SIMulation and it simulates air traffic in the enroute sector and terminal airspace for a single day and captures the measures of effectiveness at a microscopic level and feeds its output to the macroscopic annual model which then runs over the entire life cycle of the system. Airspace dwell time benefit data from the microscopic model is regressed into three dimensional benefit surfaces as a function of the equipage level of aircraft and aircraft density and embedded into the macroscopic model. The main function of the annual model is to ascertain economic viability of any deployment schedule or alternative over the entire life cycle of the system. The life cycle cost model is com-posed of four modules namely: Operational benefits module, Safety benefit module,Technology cost module and Training cost module. Analysis using the model showed that an enroute sector gets congested at aircraft den-sities greater 630 per day. This is mainly because the controller workload gets satu-rated at that traffic volume per day. Benefits realized in alternatives B, C and D as compared to alternative A increased exponentially at traffic densities greater than 630 i.e. when controller workload for alternative A becomes saturated.
Master of Science

The purpose of this Master’s Thesis is to produce a specification for the aircraft conflict alert system STCA, and implement a prototype as a module in the air traffic surveillance system NOVA9000.

The specification is constructed based on functional requirements from EUROCONTROL and describes a system using a nominal trajectory method, where the future paths of aircraft are estimated. The trajectory is created using a probabilistic approach, where future positions are described with probability fields.

The prototype is implemented using the specification with some simplifications. The prototype is evaluated using recorded traffic from a heavy air traffic region surrounding an airport with parallel runways. 15 alerts were induced in 1,5 hour of morning traffic; this is far too much to be acceptable. Improvements are proposed and explanations to the high rate of alerts are made.

Una città intelligente è un luogo in cui la tecnologia viene sfruttata per aiutare le amministrazioni pubbliche a prendere decisioni. La tecnologia può contribuire alla gestione di numerosi aspetti della vita quotidiana, offrendo ai cittadini servizi più affidabili e migliorando la qualità della vita. Tuttavia, la tecnologia da sola non è sufficiente per rendere una città intelligente; sono necessari metodi adeguati per analizzare i dati raccolti e gestirli in modo da generare informazioni utili. Alcuni esempi di servizi intelligenti sono le app che permettono di raggiungere una destinazione attraverso il percorso più breve oppure di trovare il parcheggio libero più vicino, o le app che suggeriscono i percorsi migliori per una passeggiata in base alla qualità dell'aria. Questa tesi si concentra su due aspetti delle smart city: sostenibilità e sicurezza. Il primo aspetto riguarda lo studio dell'impatto del traffico sulla qualità dell'aria attraverso lo sviluppo di una rete di sensori di traffico e qualità dell'aria e l'implementazione di una catena di modelli di simulazione. Questo lavoro fa parte del progetto TRAFAIR, cofinanziato dall'Unione Europea, il primo progetto che monitora la qualità dell'aria in tempo reale e fa previsioni su scala urbana in 6 città europee, tra cui Modena. Il progetto ha richiesto la gestione di una grande quantità di dati eterogenei e la loro integrazione su una piattaforma dati complessa e scalabile condivisa da tutti i partner del progetto. La piattaforma è un database PostgreSQL, adatto a gestire dati spazio-temporali, che contiene più di 60 tabelle e 435 GB di dati (solo per Modena). Tutti i processi della pipeline di TRAFAIR, le dashboard e le app sfruttano il database per ottenere i dati di input ed eventualmente memorizzare l'output. I modelli di simulazione, eseguiti su risorse di HPC, utilizzano i dati dei sensori e devono fornire risultati in tempo reale. Pertanto le tecniche di identificazione delle anomalie applicate ai dati dei sensori devono eseguire in tempo reale e in breve tempo. Dopo un attento studio della distribuzione dei dati dei sensori e della correlazione tra le misure, sono state implementate e applicate alcune tecniche di identificazione delle anomalie. Per i dati di traffico è stato sviluppato un nuovo approccio che utilizza un filtro di correlazione flusso-velocità, la decomposizione STL e l'analisi IQR. Per i dati di qualità dell'aria è stato creato un framework innovativo che implementa 3 algoritmi. I risultati degli esperimenti sono stati confrontati con quelli dell'Autoencoder LSTM. L'aspetto relativo alla sicurezza nella città intelligente è legato a un progetto di analisi dei crimini, i processi analitici volti a fornire informazioni tempestive e pertinenti per aiutare la polizia nella riduzione, prevenzione e valutazione del crimine. A causa della mancanza di dati ufficiali, questo progetto sfrutta le notizie pubblicate sui giornali online. L'obiettivo è quello di classificare le notizie in base alla categoria di crimine, geolocalizzare i crimini, identificare la data dell'evento, e individuare alcune caratteristiche. È stata sviluppata un'applicazione per l'analisi delle notizie, l'estrazione di informazioni semantiche attraverso l'uso di tecniche di NLP e la connessione delle entità a risorse Linked Data. La tecnologia dei Word Embedding è stata utilizzata per la categorizzazione del testo, mentre il Question Answering tramite BERT è stato utilizzato per estrarre le 5W+1H. Le notizie che si riferiscono allo stesso evento sono state identificate attraverso la cosine similarity sul testo delle notizie. Infine, è stata implementata un'interfaccia per mostrare su mappa i crimini geolocalizzati e fornire statistiche e rapporti annuali. Questo è l'unico progetto presente in Italia che partendo da notizie online cerca di fornire un'analisi sui crimini e la mette a disposizione attraverso uno strumento di visualizzazione.
A smart city is a place where technology is exploited to help public administrations make decisions. The technology can contribute to the management of multiple aspects of everyday life, offering more reliable services to citizens and improving the quality of life. However, technology alone is not enough to make a smart city; suitable methods are needed to analyze the data collected by technology and manage them in such a way as to generate useful information. Some examples of smart services are the apps that allow to reach a destination through the least busy road route or to find the nearest parking slot, or the apps that suggest better paths for a walk based on air quality. This thesis focuses on two aspects of smart cities: sustainability and safety. The first aspect concerns studying the impact of vehicular traffic on air quality through the development of a network of traffic and air quality sensors, and the implementation of a chain of simulation models. This work is part of the TRAFAIR project, co-financed by the European Union, which is the first project with the scope of monitoring in real-time and predicting air quality on an urban scale in 6 European cities, including Modena. The project required the management of a large amount of heterogeneous data and their integration on a complex and scalable data platform shared by all the partners of the project. The data platform is a PostgreSQL database, suitable for dealing with spatio-temporal data, and contains more than 60 tables and 435 GB of data (only for Modena). All the processes of the TRAFAIR pipeline, the dashboards and the mobile apps exploit the database to get the input data and, eventually, store the output, generating big data streams. The simulation models, executed on HPC resources, use the sensor data and provide results in real-time (as soon as the sensor data are stored in the database). Therefore, the anomaly detection techniques applied to sensor data need to perform in real-time in a short time. After a careful study of the distribution of the sensor data and the correlation among the measurements, several anomaly detection techniques have been implemented and applied to sensor data. A novel approach for traffic data that employs a flow-speed correlation filter, STL decomposition and IQR analysis has been developed. In addition, an innovative framework that implements 3 algorithms for anomaly detection in air quality sensor data has been created. The results of the experiments have been compared to the ones of the LSTM autoencoder, and the performances have been evaluated after the calibration process. The safety aspect in the smart city is related to a crime analysis project, the analytical processes directed at providing timely and pertinent information to assist the police in crime reduction, prevention, and evaluation. Due to the lack of official data to produce the analysis, this project exploits the news articles published in online newspapers. The goal is to categorize the news articles based on the crime category, geolocate the crime events, detect the date of the event, and identify some features (e.g. what has been stolen during the theft). A Java application has been developed for the analysis of news articles, the extraction of semantic information through the use of NLP techniques, and the connection of entities to Linked Data. The emerging technology of Word Embeddings has been employed for the text categorization, while the Question Answering through BERT has been used for extracting the 5W+1H. The news articles referring to the same event have been identified through the application of cosine similarity to the shingles of the news articles' text. Finally, a tool has been developed to show the geolocalized events and provide some statistics and annual reports. This is the only project in Italy that starting from news articles tries to provide analyses on crimes and makes them available through a visualization tool.

En milieu urbain, le trafic routier contribue de manière significative aux émissions atmosphériques, enjeu majeur de la lutte contre le changement climatique. Par conséquent, la surveillance conjointe du trafic routier et des émissions qu’il génère constitue un support essentiel de la décision publique. Au-delà de simples procédures de suivi, les pouvoirs publics ont besoin de méthodes d’évaluation des politiques de transport selon des critères environnementaux.Le couplage de modèles de trafic avec des modèles d’émissions constitue une réponse adaptée à ce besoin. Cependant, l’intégration de tels models à des outils d'aide à la décision nécessite une ca-ractérisation fine et dynamique de la mobilité urbaine. Les données de téléphonie mobile, et en particulier les statistiques d'appel (données CDR), sont une alternative aux données traditionnelles pour estimer cette mobilité. Elles sont riches, massives, et disponibles partout dans le monde. Néanmoins, leur utilisation pour la caractérisation systématique du trafic routier est restée limitée. Cela s'explique par une faible résolution spatiale et des taux d'échantillonnage temporels sensible aux comportements de communication.Cette thèse de doctorat interroge l'estimation des variables de trafic nécessaires au calcul d'émis-sions atmosphériques (distances totales parcourues et vitesses moyennes de trafic) à partir de telles données, et malgré leurs biais. Une première contribution importante est d’articuler des méthodes de classification des individus avec deux approches distinctes de reconstruction de la mobilité. Un seconde contribution est le développement d'une méthode d'estimation des vitesses de trafic basée sur la fusion de larges quantité de données de déplacements. Enfin, un processus méthodologique complet de modélisation et de traitement des données est avancé. Il articule de façon cohérente les méthodes proposées dans cette thèse
Road traffic contributes significantly to atmospheric emissions in urban areas, a major issue in the fight against climate change. Therefore, joint monitoring of road traffic and related emissions is essential for urban public decision-making. And beyond this kind of procedure, public authorities need methods for evaluating transport policies according to environmental criteria.Coupling traffic models with traffic-related emission models is a suitable response to this need. However, integrating this solution into decision support tools requires a refined and dynamic char-acterization of urban mobility. Cell phone data, particularly Call Detail Records, are an interesting alternative to traditional data to estimate this mobility. They are rich, massive, and available worldwide. However, their use in literature for systematic traffic characterization has remained limited. It is due to low spatial resolution and temporal sampling rates sensitive to communication behaviors.This Ph.D. thesis investigates the estimation of traffic variables necessary for calculating air emis-sions (total distances traveled and average traffic speeds) from such data, despite their biases. The first significant contribution is to articulate methods of classification of individuals with two distinct approaches of mobility reconstruction. A second contribution is developing a method for estimating traffic speeds based on the fusion of large amounts of travel data. Finally, we present a complete methodological process of modeling and data processing. It relates the methods proposed in this thesis coherently

L’origine anthropique du changement climatique est désormais sans équivoque, et son atténuation nécessite une réduction drastique des émissions de gaz à effet de serre. Bien que le secteur aérien soit un émetteur relativement modeste, il représente environ 2,6% des émissions de CO2 et est responsable d’autres effets, dits non-CO2, significatifs et globalement réchauffants. Plusieurs leviers de décarbonation sont disponibles pour inverser l’augmentation des émissions, notamment l’amélioration de l’efficacité des avions et des opérations, ainsi que le remplacement du kérosène fossile par des alternatives moins émettrices sur l’ensemble de leur cycle de vie. Les avantages et les inconvénients de ces différentes options, ainsi que leurs interactions, peuvent être explorés par des scénarios de transition. Bien qu’utilisés par plusieurs acteurs institutionnels, industriels ou académiques, ces scénarios manquent souvent d’une méthodologie détaillée et transparente, d’une couverture disciplinaire suffisante pour permettre des choix stratégiques éclairés, ou sont difficilement adaptables à des cas d’étude différents de leur contexte initial. Cette thèse propose trois axes permettant de répondre en partie à chacun de ces défis. Elle s’inscrit dans le cadre du développement continu d’AeroMAPS, un simulateur de scénarios prospectifs spécifique à la décarbonation du transport aérien. Premièrement, comme les opportunités de décarbonation varient d’une région à l’autre (ressources naturelles et financières, niveaux de trafic...), l’adaptabilité des scénarios de transition à ces différentes échelles est un défi clé. Cette thèse contribue à y répondre en présentant une méthode permettant d’estimer les flux de trafic aérien et les émissions de CO2 associées, de manière open-source, reproductible et segmentable. Ces données sont utilisées par AeroMAPS pour générer des scénarios à des échelles réduites, comme celle d’un continent ou d’un pays. Elles sont également utilisées dans cette thèse pour étudier l’utilisation du transport aérien à travers le monde, révélant des inégalités marquées. Ensuite, ce travail enrichit la modélisation des scénarios de transition en adaptant et en intégrant différents modèles de coûts. En particulier, l’évolution des coûts de l’énergie pour le secteur aérien est étudiée en adaptant et en intégrant différents modèles repris de la littérature spécialisée. Ceux-ci permettent d’estimer les prix de vente minimaux des carburants alternatifs bas-carbone étudiés pour l’aviation. L’impact global des différents leviers de décarbonation sur les coûts des compagnies aériennes est modélisé en intégrant un modèle représentant leurs coûts d’exploitation. Ce modèle est aussi utilisé dansces travaux pour étudier l’impact techno-économique d’architectures avions redéfinies dans un contexte environnemental contraint. Enfin, l’efficacité économique des différents leviers de décarbonation est examinée via deux approches. D’une part, celle-ci peut être évaluée grâce à des métriques de coûts d’abattement du carbone combinées à des courbes de coûts marginaux d’abattement sectoriels, que ces travaux ont adaptées et développées pour les scénarios de transition. Cela permet notamment de comparer les coûts de la décarbonation du transport aérien avec différentes valeurs carbone de référence. D’autre part, la mise en place d’une approche permettant de minimiser les coûts des scénarios de transition permet d’aborder la question de manière plus complète, via une approche de coût-efficacité. Une application est proposée pour redéfinir les mandats d’incorporation de carburant alternatifs prévus pour l’aviation européenne par la législation ReFuelEU, sous différentes contraintes de budget carbone et de disponibilité des ressources énergétiques
The anthropogenic origin of climate change is now unequivocal, and its mitigation requires drastic reductions in greenhouse gas emissions. Although aviation is a relatively moderate emitter, it still accounts for about 2.6% of CO2 emissions and is responsible for significantnon-CO2, globally warming effects. Several decarbonisation levers are available to reverse the upward emissions trend, including further improvements in aircraft and operational efficiency and the replacement of fossil kerosene with low-carbon alternatives. The advantages and disadvantages of these different options, as well as their interactions, can be explored in prospective transition scenarios. Although they are used by several institutional, industrial or academic stakeholders, they often lack either a detailed and transparent methodology, sufficient disciplinary coverage to make informed strategic choices, or limited adaptability to different cases of application. This thesis proposes three areas of improvement to address these issues, as part of the continuing development of AeroMAPS, a prospective scenario simulator specific to air transport decarbonisation. First, as decarbonisation opportunities vary from region to region (natural and financial resources, traffic levels...), the adaptability of transition scenarios to these different scales is a key issue. This thesis contributes to addressing this issue by presenting a method for estimating air traffic and CO2 emission flows in an open source, reproducible and partitionable manner. These data are used in AeroMAPS to generate scenarios at reduced scales, such as a continent or a country. They are also used to study the air transport use around the world, revealing strong inequalities. Then, this work enriches the modelling of transition scenarios by adapting and integrating different cost models into the same framework. In particular, models from the literature for estimating the minimum selling prices of various alternative low-carbon fuels are used to study the evolution of energy costs for the aviation sector. Similarly, operational cost models are implemented to model the overall impact of different decarbonisation levers on airline costs. These models are also used to study the technical and economic impact of aircraft architectures in a constrained environmental context. Lastly, the economic efficiency of the different decarbonisation levers is examined using two approaches. On the one hand, it can be assessed using carbon abatement cost metrics and sectoral marginal abatement cost curves adapted and developed for the context of transition scenarios. In particular, this makes it possible to compare the costs of decarbonising air transport with different reference carbon values. On the other hand, a more comprehensive cost-effectiveness approach is presented through cost optimisation of transition scenarios. An application is proposed to challenge the fuel blending mandates of the ReFuelEU legislation for European aviation for different carbon budget and energy resource constraints

Com o crescimento do setor aeronáutico faz-se necessário a criação de novas tecnologias para que a capacidade do sistema possa aumentar sem provocar perdas nos níveis de segurança. Para isso foi criado o CNS/ATM, um paradigma que integra tecnologias de comunicação, navegação e vigilância em um sistema de gerenciamento de tráfego aéreo global. No âmbito das comunicações entre controlador e piloto, o atual sistema de fonia via rádio analógico é substituído por um enlace de dados, chamado de Controller-Pilot Data Link Communication (CPDLC). Esta alteração promove polêmica entre aeronautas e autoridades, de forma que é essencial um estudo aprofundado que comprove sua eficácia. Dessa forma, esta pesquisa visa avaliar a segurança do CPDLC quando utilizado em Áreas Terminais do Espaço Aéreo. Para a realização desta pesquisa, foi criada uma metodologia de avaliação de segurança utilizando simuladores de voo, de tráfego aéreo e de comunicação CPDLC combinados com uma análise por meio de modelos de Markov. O procedimento de chegada ao aeroporto de Congonhas, situado na Terminal São Paulo, foi utilizado como referência para aplicação desta metodologia. Os resultados obtidos mostraram que o sistema atual de comunicação, via voz, está no limite do atendimento dos níveis internacionais de segurança para a demanda atual. Contudo, a comunicação por enlace de dados atende, e em alguns casos inclusive melhora, o nível de segurança desta região do espaço aéreo.
With the growth of the aviation industry it is necessary to create new technologies that can increase the system capacity without loss in the safety levels. Because of this the CNS/ATM was created, a paradigm that integrates communication, navigation and surveillance technologies with a global air traffic management. For the communications between controllers and pilots, the current system of voice over analog radio is being replaced by a data link, called the Controller-Pilot Data Link Communication (CPDLC). This replacement raises debates between airmen and authorities, demanding a detailed study that proves its effectiveness. Thus, this research aims to evaluate the CPDLC safety when used in Terminal Airspace Areas. For this research, was created a methodology for security assessment using flight, air traffic, and CPDLC simulators combined with a Markov model analysis. The Congonhas airport arrival procedure, located in São Paulo Terminal, was used as reference for this methodology application. The results showed that the current system of voice communication is on the limit of fulfilling the international safety levels for the current demand. However, the data link communication addresses, and in some cases even improve, the safety level for this airspace region.

Idag är många branscher beroende av ett gediget teamwork. Det finns dock ett behov av objektiva mätsystem för teamwork och därför har detta projekt som syfte att skapa och testa ett observationsprotokoll utifrån den teoretiska modellen ’The Big Five of Teamwork’ framtagen av Salas, Sims & Burke (2005). Observationsprotokollet användes för att observera teamwork mellan två flygledare på Arlanda ATCC. Därefter fick flygledarna svara på en enkät för att bidra med subjektiva aspekter från modellen. Totalt genomfördes 15 stycken strukturerade observationer. Resultatet visade att det är möjligt att skatta teamwork på flygledare med hjälp av ett observationsprotokoll baserat på sex av åtta komponenter, där inte teamorientering och gemensam mental modell ingick. Komponenterna visade sig vara mer än bara ett observerbart beteende och enbart observationer frambringar inte en rättvis bild över komponenten. Resultatet visade också på att flygledarna själva upplever samtliga komponenter som en del av arbetet. Observationerna visade att samarbetet kunde se olika ut och skilja sig från team till team, och att flygledarna anpassar sig efter varandras behov.
Today, many industries are dependent on a solid teamwork. However, there is a need for objective measurement assessment for teamwork and therefore this project aims to create and test an observation protocol based on the theoretical model ’The Big Five of Teamwork’ compiled by Salas, Sims & Burke (2005). The observation protocol was used to observe teamwork between two air traffic controllers at Arlanda ATCC. After the observations the air traffic controllers answered a survey to receive subjective aspects from the model. A total of 15 structured observations were conducted. The results revealed that it’s possible to estimate teamwork on air traffic controllers using an observation protocol based on six of eight components, where team orientation and shared mental model were not included. The components appeared to be more than just an observable behavior, thus only observations does not give a fair picture of the component. The result also showed that air traffic controllers themselves perceive all components as a part of the work. The observations showed that the cooperation could look different and differ from team to team, and that air traffic controllers adapt to each other’s needs.

Les visualisations de données permettent de transmettre de l’information aux utilisateurs. Pour explorer et comprendre les données, les utilisateurs sont amenés à interagir avec ces visualisations.Toutefois, l’interaction avec les visualisations modifie le visuel. Pour éviter des changements brusques et garder l’utilisateur focalisé sur les objets graphiques d’intérêt, des transitions visuelles sont nécessaires pour accompagner les modifications de la visualisation. Ces transitions visuelles peuvent être codées sous la forme d’animations, ou de techniques qui permettent de faire des correspondances, ou des liens avec des données représentées sur plusieurs affichages. Le premier objectif de cette thèse était d’étudier les bénéfices et les propriétés des animations pour l’exploration et la compréhension de grandes quantités de données multidimensionnelles. Nous avons établi en conséquence une taxonomie des transitions animées en visualisation d’information basée sur les tâches des utilisateurs. Cette taxonomie a permis de constater qu’il n’existe pas de contrôle utilisateur sur la direction des objets durant l’animation. Nous avons donc proposé des interactions pour le contrôle de la direction des objets graphiques lors d’une transition animée. D’autre part, nous avons étudié une technique de transition animée mettant en jeu une rotation 3D entre visualisations. Nous avons identifié les avantages qu’elle pouvait apporter et en avons proposé une amélioration.Le second objectif était d’étudier les transitions visuelles dans le domaine du Contrôle du Trafic Aérien. En effet, les contrôleurs utilisent de nombreuses visualisations qui comportent des informations étalées et dupliquées sur plusieurs affichages: l’écran Radar, le tableau de strips, des listes spécifiques d’avions (départ, arrivées) etc. Ainsi dans leur activité, les Contrôleurs Aériens réalisent des transitions visuelles en recherchant et en reliant de l’information à travers les différents affichages. Nous avons étudié comment les animations pouvaient être utilisées dans le domaine du contrôle aérien en implémentant un prototype d’image radar regroupant trois visualisations usuelles pour instrumenter l’activité de supervision du trafic aérien
Data visualizations allow information to be transmitted to users. In order to explore and understand the data, it is often necessary for users to manipulate the display of this data. When manipulating the visualization, visual transitions are necessary to avoid abrupt changes in this visualization, and to allow the user to focus on the graphical object of interest. These visual transitions can be coded as an animation, or techniques that link the data across several displays. The first aim of this thesis was to examine the benefits and properties of animated transitions used to explore and understand large quantities of multidimensional data. In order to do so, we created a taxonomy of existing animated transitions. This taxonomy allowed us to identify that no animated transition currently exists that allows the user to control the direction of objects during the transition. We therefore proposed an animated transition that allows the user to have this control during the animation. In addition, we studied an animated transition technique that uses 3D rotation to transition between visualizations. We identified the advantages of this technique and propose an improvement to the current design. The second objective was to study the visual transitions used in the Air Traffic Control domain. Air Traffic Controllers use a number of visualizations to view vast information which is duplicated in several places: the Radar screen, the strip board, airplane lists (departures/arrivals) etc. Air traffic controllers perform visual transitions as they search between these different displays of information. We studied the way animations can be used in the Air Traffic Control domain by implementing a radar image prototype which combines three visualizations typically used by Air Traffic Controllers

Le transport aérien est fondé sur l'utilisation de l'avion pour transporter des passagers entre deux aéroports, et son développement est allé de pair avec l'amélioration continue de l'efficacité et de la sécurité des avions comme moyens de transport. Cependant, si la pandémie liée au COVID-19 nous a appris une leçon, c'est qu'un problème qui touche les passagers du transport aérien peut avoir bien plus de conséquences sur le système dans son ensemble qu'un problème qui concerne les avions. Partant du principe que les passagers sont omniprésents et nécessaires au transport aérien, cette thèse propose de considérer les passagers comme des capteurs du transport aérien, et d'utiliser les données générées par les passagers pour évaluer la performance du transport aérien en quasi temps réel. Ces données générées par les passagers ont également l'avantage d'offrir un moyen d'évaluer les interactions entre les passagers et les autres acteurs du transport aérien, en particulier les aéroports et les compagnies aériennes. Comme le parcours d'un passager commence et se termine au delà des limites d'un aéroport, les données générées par les passagers tout au long de ce parcours peuvent également être utilisées pour évaluer le trajet porte-à-porte complet d'un passager du transport aérien
Air transportation uses planes to transport passengers efficiently between two airports, and its development has been driven by the continuous improvement of planes as a safe and efficient means of transportation. However, if the COVID-19 pandemic has taught the air transportation system one lesson, it's that a problem affecting passengers can be far more detrimental to the air transportation system than a problem affecting planes. Acknowledging the fact that passengers are omnipresent and necessary to the air transportation system, this study proposes to consider passengers as sensors of the air transportation system and harness data generated by passengers to evaluate in near real time the flight-centric metrics traditionally used to evaluate the air transportation system performance. Data generated by passengers have the additional benefit of offering a means of evaluating the interactions between passengers and the other stakeholders of the air transportation system, such as airlines and airports. The journey of a passenger starting and ending beyond the boundaries of airport facilities, the data generated by passengers throughout their journey can also be used to evaluate the full door-to-door journey of a passenger of the air transportation system

Due to the increase of air traffic that has occurred in recent years and that is expected to continue in the near future, it is necessary to take measures to ensure air traffic control systems can keep up with this tendency. These measures also aim to prevent cost escalation as well as deal with the increasing scarcity of radio frequencies. It is in this context that a new technology called Controller Pilot Data Link Communication(CPDLC) emerges, which aims to replace voice communications between pilots and air traffic controllers with a text messaging service for non-critical situations, applicable to aircrafts above a certain altitude. My project aimed at augmenting an existing simulator for air traffic control, called SIMATM, to support this new CPDLC functionality. The execution of this project followed the V-model of software development that has been adopted by NAV Portugal, E.P.E. The main deliverables are, on the one hand, a new Data Link Server simulator, or SIMDLS, whose development was decided during the problem analysis phase, and, on the other hand, the evolution of the internal components of the SIMATM simulator so that it supports CPDLC. Another deliverable of this project is the publication of a scientific paper in the 6th Iberian Conference on Information Systems and Technologies.

Air traffic controllers (ATCos) monitor flight operations and resolve predicted aircraft conflicts to ensure safe flights, making them one of the essential human operators in air traffic control systems. Researchers have been studying ATCos with human subjective approaches to understand their tasks and air traffic managing processes. As a result, models were developed to predict ATCo actions. However, there is a gap between our knowledge and the real-world. The developed models have never been validated against the real-world, which creates uncertainties in our understanding of how ATCos detect and resolve predicted aircraft conflicts. Moreover, we do not know how information from air traffic control systems affects their actions. This Ph.D. dissertation work introduces methods to evaluate existing ATCo action prediction models. It develops a prediction model based on flight contextual information (information describing flight operations) to explain the relationship between ATCo actions and information. Unlike conventional approaches, this work takes data-driven approaches that collect large-scale flight tracking data. From the collected real-world data, ATCo actions and corresponding predicted aircraft conflicts were identified by developed algorithms. Comparison methods were developed to measure both qualitative and quantitative differences between solutions from the existing prediction models and ATCo actions on the same aircraft conflicts. The collected data is further utilized to develop an ATCo action prediction model. A hierarchical structure found from analyzing the collected ATCo actions was applied to build a structure for the model. The flight contextual information generated from the collected data was used to predict the actions. Results from this work found that the collected ATCo actions do not show any preferences on the methods to resolve aircraft conflicts. Results found that the evaluated existing prediction model does not reflect the real-world. Also, a large portion of the real conflicts was to be solved by the model both physically and operationally. Lastly, the developed prediction model showed a clear relationship between ATCo actions and applied flight contextual information. These results suggest the following takeaways. First, human actions can be identified from closed-loop data. It could be an alternative approach to collect human subjective data. Second, the importance of evaluating models before implications. Third, potentials to utilize the flight contextual information to conduct high-end prediction models.

碩士
國防大學
資訊管理學系
102
The Ministry of National Defense of the Republic of China promulgated “development plan of military cloud service” on March 31, 2011. It indicated the short, medium, and long term development goals of the military cloud service in 2011, and hoped that improving the deployment of resources and information security by establishing the environment of cloud computing under the downsizing situation. Therefore, the information security can be monitored easily to perform missions effectively. However, there are still some security issues for cloud computing. From the point of view of the traditional access control method, air force is going to build air traffic control simulation & training system, wherein air traffic controller (ATC) are divided into different seats with distinct missions according to the distinct coordination and control area. In accordance with capability, each seat is dividesd into three levels of qualified, skilled, and instructor. Each seat should be interactive experience and reach the instructor level. Therefore, everyone will obtain the distinct service permission of the necessary training through the cloud system. Accordingly, management of access permission still has to rely on a huge database to store a large number of data for user permissions, consequently, the memory space is wasted seriously or causing management burden and time-wasting due to the permission transaction. Therefore, this study proposes a dynamic access control method of air traffic control simulation & training system for air force, to enhance the flexibility and security of service access.

碩士
輔仁大學
公共衛生學系碩士班
99
Traffic policemen often experience relatively high levels of traffic related air pollutants in busy urban streets. Although some studies had been performed on exposure assessment of traffic policemen in Taiwan, few studies was evaluated the feasibility using fixed air monitoring station (AMS) data to assess personal exposure levels. The objective of this study was to investigate exposure of air pollutants in traffic policemen who worked nearby Sanchong traffic AMS, and to evaluate the association between personal exposure levels and data from the traffic AMS. The exposure levels of air pollutants on duty rush hour in traffic policemen were evaluated by portable direct reading instruments. The measured pollutants included particulatematter(PM), carbon monoxide(CO),carbon dioxide(CO2), particulate-bounded polycyclic aromatic hydrocarbons(PAHs),and volatile organic compounds(VOCs). The meteorological data were collected by the wind speed anemoscope. Simultaneous pollutants data from the traffic AMS were downloaded for correlation analysis. Data were collected from November 2008 to March 2010. And demonstrated that the personal exposure (mean±SD) to PM1, PM2.5, PM10, CO, CO2, VOCs, and PAHs were 26.81±18.64μg/m3, 34.78 ±22.27μg/m3, 55.54±32.01μg/m3, 6.66±2.66 ppm, 443.57±53.86 ppm, 215.27±199.24 ppb, and198.74±71.78 ng/m3, respectively. Wind speed and Ambient temperature were moderately negatively related to personal air pollutants exposure and were positive related to relative humidity. The Pierson correlation coefficient between personal exposure value and data from traffic air quality monitoring station on PM2.5, PM10, and CO were 0.720, 0.748, and 0.158, respectively. It suggested that the PM level in Sanchong traffic AMS would be better to predict the PM exposure of traffic policemen, rather than the gaseous pollutants.

碩士
國防醫學院
公共衛生學研究所
100
There are more and more research demonstration that air pollutant has bad effects on the human body and health. The result from monitoring stations is unable to represent the pollutant condition in all places, therefore, establishment of pollutant models to predict the pollutant concentration of unknown area is the tendency also necessary. Land Use Regression (LUR) contained the data from geography infor-mation system (GIS) had already succeed to predict the spatial variation of the air pollutant concentration in the past research, and demonstrated that its results are better than other models. Base on the previous research in European and American, this research establishes the simple linear regression models of the transportation related pollutant include the carbon monoxide (CO), the nitrogen oxide (NOx), the ozone (O3), and the suspended particulate matter (PM10) in Taipei metropolitan area using the Taiwan Environmental Protection Agency (EPA) air quality monitoring station information from year 2006 to 2009, and using data from Taipei City Environmental Protection Agency monitoring station for verification, to investigate the models are suitable and impacts. In lacks of the traffic flow information, this findings discovered that CO and the NOx models (r2 = 0.76, 0.67) predictive ability are better than O3 and the PM10 (r2 = 0.27, 0.19) and match the results of past studies, and we might know that models creation using the annual mean to compare in the monthly mean effect is better, but used the maximum value establishment models not to achieve the expectation effect.

Tese de mestrado em Engenharia Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2011
Com o aumento de tráfego aéreo que tem ocorrido nos últimos anos e com o que se prevê que venha a acontecer num futuro próximo é necessário começar a tomar medidas para que os sistemas de controlo de tráfego aéreo suportem este aumento. Estas medidas também têm como objectivo evitar que os custos aumentem com o aumento de tráfego aéreo bem como ultrapassar a escassez cada vez maior de frequências rádio. É neste âmbito que surge uma nova tecnologia denominada de Controller Pilot Data Link Communication (CPDLC, esta tecnologia pretende substituir a comunicação por voz efectuada entre o piloto e o controlador de tráfego aéreo por comunicação por texto, tratando-se de um serviço de mensagens escritas para situações não críticas e aplicável a aeronaves acima de determinada altitude (altitude de rota). Este relatório descreve o evoluir de um simulador de controlo de tráfego aéreo existente, designado SIMATM, para que este suporte a nova funcionalidade CPDLC. A realização do projecto seguiu o modelo de desenvolvimento de software em V usado na NAV Portugal, E.P.E., que se traduziu na adição de um simulador de Data Link Server ou SIMDLS, criado de raiz, sendo que a sua necessidade foi decidida no decurso do projecto, durante a análise do problema, bem como na evolução das componentes internas do SIMATM. Resultante da realização do projecto foi também a produção e publicação de um artigo científico para a 6ª Conferência Ibérica de Sistemas e Tecnologias de Informação.
Due to the increase of air traffic that has occurred in recent years and that is expected to continue in the near future, it is necessary to take measures to ensure air traffic control systems can keep up with this tendency. These measures also aim to prevent cost escalation as well as deal with the increasing scarcity of radio frequencies. It is in this context that a new technology called Controller Pilot Data Link Communication (CPDLC) emerges, which aims to replace voice communications between pilots and air traffic controllers with a text messaging service for non-critical situations, applicable to aircrafts above a certain altitude. My project aimed at augmenting an existing simulator for air traffic control, called SIMATM, to support this new CPDLC functionality. The execution of this project followed the V-model of software development that has been adopted by NAV Portugal, E.P.E. The main deliverables are, on the one hand, a new Data Link Server simulator, or SIMDLS, whose development was decided during the problem analysis phase, and, on the other hand, the evolution of the internal components of the SIMATM simulator so that it supports CPDLC. Another deliverable of this project is the publication of a scientific paper in the 6th Iberian Conference on Information Systems and Technologies.

For the past few years, oceans have become once again, an important means of communication and transport. In fact, traffic density throughout the globe has suffered a substantial growth, which has risen some concerns. With this expansion, the need to achieve a high Maritime Situational Awareness (MSA) is imperative. At the present time, this need may be more easily fulfilled thanks to the vast amount of data available regarding maritime traffic. However, this brings in another issue: data overload. Currently, there are so many data sources, so many data to obtain information from, that the operators cannot handle it. There is a pressing need for systems that help to sift through all the data, analysing and correlating, helping in this way the decision making process. In this dissertation, the main goal is to use different sources of data in order to detect anomalies and contribute to a clear Recognised Maritime Picture (RMP). In order to do so, it is necessary to know what types of data exist and which ones are available for further analysis. The data chosen for this dissertation was Automatic Identification System (AIS) and Monitorização Contínua das Atividades da Pesca (MONICAP) data, also known as Vessel Monitoring System (VMS) data. In order to store 1 year worth of AIS and MONICAP data, a PostgreSQL database was created. To analyse and draw conclusions from the data, a data mining tool was used, namely, Orange. Tests were conducted in order to assess the correlation between data sources and find anomalies. The importance of data correlation has never been so important and with this dissertation the aim is to show that there is a simple and effective way to get answers from great amounts of data.
Nos últimos anos, os oceanos tornaram-se, mais uma vez, um importante meio de comunicação e transporte. De facto, a densidade de tráfego global sofreu um crescimento substancial, o que levantou algumas preocupações. Com esta expansão, a necessidade de atingir um elevado Conhecimento Situacional Marítimo (CSM) é imperativa. Hoje em dia, esta necessidade pode ser satisfeita mais facilmente graças à vasta quantidade de dados disponíveis de tráfego marítimo. No entanto, isso leva a outra questão: sobrecarga de dados. Atualmente existem tantas fontes de dados, tantos dados dos quais extrair informação, que os operadores não conseguem acompanhar. Existe uma necessidade premente para sistemas que ajudem a escrutinar todos os dados, analisando e correlacionando, contribuindo desta maneira ao processo de tomada de decisão. Nesta dissertação, o principal objetivo é usar diferentes fontes de dados para detetar anomalias e contribuir para uma clara Recognised Maritime Picture (RMP). Para tal, é necessário saber que tipos de dados existem e quais é que se encontram disponíveis para análise posterior. Os dados escolhidos para esta dissertação foram dados Automatic Identification System (AIS) e dados de Monitorização Contínua das Atividades da Pesca (MONICAP), também conhecidos como dados de Vessel Monitoring System (VMS). De forma a armazenar dados correspondentes a um ano de AIS e MONICAP, foi criada uma base de dados em PostgreSQL. Para analisar e retirar conclusões, foi utilizada uma ferramenta de data mining, nomeadamente, o Orange. De modo a que pudesse ser avaliada a correlação entre fontes de dados e serem detetadas anomalias foram realizados vários testes. A correlação de dados nunca foi tão importante e pretende-se com esta dissertação mostrar que existe uma forma simples e eficaz de obter respostas de grandes quantidades de dados

Abstracts in English, Afrikaans and Northern Sotho
More time series forecasting methods were researched and made available in recent years. This is mainly due to the emergence of machine learning methods which also found applicability in time series forecasting. The emergence of a variety of methods and their variants presents a challenge when choosing appropriate forecasting methods. This study explored the performance of four advanced forecasting methods: autoregressive integrated moving averages (ARIMA); artificial neural networks (ANN); support vector machines (SVM) and regression models with ARIMA errors. To improve their performance, bagging was also applied. The performance of the different methods was illustrated using South African air passenger data collected for planning purposes by the Airports Company South Africa (ACSA). The dissertation discussed the different forecasting methods at length. Characteristics such as strengths and weaknesses and the applicability of the methods were explored. Some of the most popular forecast accuracy measures were discussed in order to understand how they could be used in the performance evaluation of the methods. It was found that the regression model with ARIMA errors outperformed all the other methods, followed by the ARIMA model. These findings are in line with the general findings in the literature. The ANN method is prone to overfitting and this was evident from the results of the training and the test data sets. The bagged models showed mixed results with marginal improvement on some of the methods for some performance measures. It could be concluded that the traditional statistical forecasting methods (ARIMA and the regression model with ARIMA errors) performed better than the machine learning methods (ANN and SVM) on this data set, based on the measures of accuracy used. This calls for more research regarding the applicability of the machine learning methods to time series forecasting which will assist in understanding and improving their performance against the traditional statistical methods
Die afgelope tyd is verskeie tydreeksvooruitskattingsmetodes ondersoek as gevolg van die ontwikkeling van masjienleermetodes met toepassings in die vooruitskatting van tydreekse. Die nuwe metodes en hulle variante laat ʼn groot keuse tussen vooruitskattingsmetodes. Hierdie studie ondersoek die werkverrigting van vier gevorderde vooruitskattingsmetodes: outoregressiewe, geïntegreerde bewegende gemiddeldes (ARIMA), kunsmatige neurale netwerke (ANN), steunvektormasjiene (SVM) en regressiemodelle met ARIMA-foute. Skoenlussaamvoeging is gebruik om die prestasie van die metodes te verbeter. Die prestasie van die vier metodes is vergelyk deur hulle toe te pas op Suid-Afrikaanse lugpassasiersdata wat deur die Suid-Afrikaanse Lughawensmaatskappy (ACSA) vir beplanning ingesamel is. Hierdie verhandeling beskryf die verskillende vooruitskattingsmetodes omvattend. Sowel die positiewe as die negatiewe eienskappe en die toepasbaarheid van die metodes is uitgelig. Bekende prestasiemaatstawwe is ondersoek om die prestasie van die metodes te evalueer. Die regressiemodel met ARIMA-foute en die ARIMA-model het die beste van die vier metodes gevaar. Hierdie bevinding strook met dié in die literatuur. Dat die ANN-metode na oormatige passing neig, is deur die resultate van die opleidings- en toetsdatastelle bevestig. Die skoenlussamevoegingsmodelle het gemengde resultate opgelewer en in sommige prestasiemaatstawwe vir party metodes marginaal verbeter. Op grond van die waardes van die prestasiemaatstawwe wat in hierdie studie gebruik is, kan die gevolgtrekking gemaak word dat die tradisionele statistiese vooruitskattingsmetodes (ARIMA en regressie met ARIMA-foute) op die gekose datastel beter as die masjienleermetodes (ANN en SVM) presteer het. Dit dui op die behoefte aan verdere navorsing oor die toepaslikheid van tydreeksvooruitskatting met masjienleermetodes om hul prestasie vergeleke met dié van die tradisionele metodes te verbeter.
Go nyakišišitšwe ka ga mekgwa ye mentši ya go akanya ka ga molokoloko wa dinako le go dirwa gore e hwetšagale mo mengwageng ye e sa tšwago go feta. Se k e k a le b a k a la g o t šwelela ga mekgwa ya go ithuta ya go diriša metšhene yeo le yona e ilego ya dirišwa ka kakanyong ya molokolokong wa dinako. Go t šwelela ga mehutahuta ya mekgwa le go fapafapana ga yona go tšweletša tlhohlo ge go kgethwa mekgwa ya maleba ya go akanya. Dinyakišišo tše di lekodišišitše go šoma ga mekgwa ye mene ya go akanya yeo e gatetšego pele e lego: ditekanyotshepelo tšeo di kopantšwego tša poelomorago ya maitirišo (ARIMA); dinetweke tša maitirelo tša nyurale (ANN); metšhene ya bekthara ya thekgo (SVM); le mekgwa ya poelomorago yeo e nago le diphošo tša ARIMA. Go kaonafatša go šoma ga yona, nepagalo ya go ithuta ka metšhene le yona e dirišitšwe. Go šoma ga mekgwa ye e fepafapanego go laeditšwe ka go šomiša tshedimošo ya banamedi ba difofane ba Afrika Borwa yeo e kgobokeditšwego mabakeng a dipeakanyo ke Khamphani ya Maemafofane ya Afrika Borwa (ACSA). Sengwalwanyaki šišo se ahlaahlile mekgwa ya kakanyo ye e fapafapanego ka bophara. Dipharologanyi tša go swana le maatla le bofokodi le go dirišega ga mekgwa di ile tša šomišwa. Magato a mangwe ao a tumilego kudu a kakanyo ye e nepagetšego a ile a ahlaahlwa ka nepo ya go kwešiša ka fao a ka šomišwago ka gona ka tshekatshekong ya go šoma ga mekgwa ye. Go hweditšwe gore mokgwa wa poelomorago wa go ba le diphošo tša ARIMA o phadile mekgwa ye mengwe ka moka, gwa latela mokgwa wa ARIMA. Dikutollo tše di sepelelana le dikutollo ka kakaretšo ka dingwaleng. Mo k gwa wa ANN o ka fela o fetišiša gomme se se bonagetše go dipoelo tša tlhahlo le dihlo pha t ša teko ya tshedimošo. Mekgwa ya nepagalo ya go ithuta ka metšhene e bontšhitše dipoelo tšeo di hlakantšwego tšeo di nago le kaonafalo ye kgolo go ye mengwe mekgwa ya go ela go phethagatšwa ga mešomo. Go ka phethwa ka gore mekgwa ya setlwaedi ya go akanya dipalopalo (ARIMA le mokgwa wa poelomorago wa go ba le diphošo tša ARIMA) e šomile bokaone go phala mekgwa ya go ithuta ka metšhene (ANN le SVM) ka mo go sehlopha se sa tshedimošo, go eya ka magato a nepagalo ya magato ao a šomišitšwego. Se se nyaka gore go dirwe dinyakišišo tše dingwe mabapi le go dirišega ga mekgwa ya go ithuta ka metšhene mabapi le go akanya molokoloko wa dinako, e lego seo se tlago thuša go kwešiša le go kaonafatša go šoma ga yona kgahlanong le mekgwa ya setlwaedi ya dipalopalo.
Decision Sciences
M. Sc. (Operations Research)

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.