Colours play an important role in our everyday life. Yet, it is something that we might not pay too much attention to, it is just there, even if we may have our favourite colours and likewise. However, sometimes the colours have a very specific meaning and is a medium of communication. One example of this is air traffic control systems as the one used in Sweden, Denmark, Austria, Ireland, and Croatia. However, despite using the same system, all but Denmark and Sweden use different colour schemes in the human computer interface of the radar screens. A decision was taken within the common organisation, COOPANS, to change this and harmonize the colour scheme, but how will that be received by the users, the air traffic controllers? This thesis aimed at investigating how usable the controllers in the different countries, except Croatia, found the new colour scheme and how easy they thought it would be to adapt to. The question was how this was affected by the fact they are using different colour schemes today? Data was collected with questionnaires during simulations in high fidelity simulator platforms at the air traffic control centres in Malmö, Copenhagen, Vienna, and Shannon. It was found that there were some differences between the sites which could not be explained by the controlled for factors, age, gender, and experience. Among the differences found, one was that the perceived usability differed between controllers in Malmö and Copenhagen respectively. Hence, since they are using the same colours today, the differences seem to be a result of expectations and opinions about the current colour schemes rather than exactly which colour scheme that are currently used. There was also a trend that the opinions from the first impression seemed to be reinforced within the group during the simulation. The major differences however were found to be on individual level.

Air Traffic Control (ATC) is a safety-critical system which places high demands on air traffic controllers’ (ATCO) multitasking abilities. Having the requisite information for well-informed decision making is central, and as new technologies such as remote towers demand an increase in capacity, efficiency, and safety there is a need for research that informs system development. Adopting a systems perspective, Distributed Cognition is an approach for investigating system functioning, and Resilience Engineering is a way of observing safety factors in everyday work. The purpose of this study is to understand how air traffic controllers work from a distributed cognition perspective, and manage safety in everyday tasks from a resilience perspective. Six observations and six interviews were conducted in a Swedish control tower. The data was analyzed using Distributed Cognition for Teamwork (DiCoT) and Resilience markers (REM), which both focus on the transformation and propagation of information. The results of DiCoT show how cognitive processes in ATCO work are supported in models of physical layout, artefacts, information flow, social organization, and evolutionary design. The results of REM show potential for resilience enhancing behavior in several episodes of ATCO work. Moreover, the results suggest that methods such as DiCoT and REM may work well in the ATC domain, as well as complementary to each other. The results may be used for informing system development, and enable a before-and-after study as the control tower of study will be transformed into a remote tower.

As air traffic is forecasted to increase, air traffic control software subsequently needs to be more sophisticated. To efficiently push development forward, testing is important in order to determine usability. The tests need to be adapted to fit a particular purpose and carried out with methods that preserve the validity of the results. This thesis describes an implementation project carried out at the EUROCONTROL Experimental Centre, Bretigny-sur-Orge, France. The purpose of the project is to create an application that enables a user to create datasets of air traffic to be used for these tests. The application allows for manual work or bulk imports from external data sources. Furthermore it compiles scenarios as output datasets intended for prototype air traffic control software developed at Linköping University. The application design rationale and development process is described. Some time is spent on demonstrating the flexibility of the application and how its usage fits in a bigger picture.

There are a lot more roads being built periodically in most of the countries with the advancement of modern society. In order to promote the overall traffic flow quality within different cities, city traffic management has been playing a more and more essential role during the last few decades. In recent years, a significantly increasing attention has been paid to the management of traffic flow in major cities all over the world. In this thesis, we develop a stochastic dynamic model for urban traffic along with physical constraints characteristic of intersections equipped with traffic light. We assume that the incoming traffic to each stream in an intersection is amenable to the Poisson random process with variable intensity (mean). We introduce expressions for traffic throughput, congestion as well as operator's waiting time for the typical intersection in a city and hereafter define an appropriate objective functional. Afterwards, we formulate an optimization problem and propose the sequential (or recursive) algorithm based on the principle of optimality (dynamic programming) due to Bellman. The solution if implemented is expected to improve throughput, reduce congestion, and promote driver's satisfaction. Because the dynamic programming method is computationally quite intensive, we consider the scenario that one unit traffic stream stands for a specific number of vehicles which actually depends on the volume of traffic flow through the intersection. The system is simulated with inputs described by several distinct nonhomogeneous Poisson processes. For example, we apply the typical traffic arrival rate in Canada with morning peak hour at around 7:30 AM and afternoon peak hour at around 4:30 PM whilst it is also applied with morning rush hour at about 8:00 AM and afternoon rush hour at about 6:00 PM like in China. In the meanwhile, we also present a group of numerical results for the traffic arrival rates that have shorter morning peak-hour period but longer afternoon rush hour period. This may occasionally happen when there are some social activities or big events in the afternoon. In addition, another series of experiments are carried out to illustrate the feasibility of the proposed dynamic model based on the traffic arrival rates with only one peak-hour throughout the whole day. The system is simulated with a series of experiments and the optimization problem is solved by dynamic programming based on the proposed algorithm which gives us the optimal feedback control law. More specifically, the results show that both the optimal traffic light timing allocated for each stream and the congestion broadcast level (CBL) of each road segment during each time segment are found. Accordingly, the corresponding optimal cost can be found for any given initial condition. It is reasonably believed that this stochastic dynamic model would be potentially applicable for real time adaptive traffic control system.

The control of multiple unmanned aerial vehicles (UAVs) in a swarm or cooperative team scenario has been a topic of great interest for well over a decade, growing steadily with the advancements in UAV technologies. In the academic community, a majority of the studies conducted rely on simulation to test developed control strategies, with only a few institutions known to have nurtured the infrastructure required to propel multiple UAV control studies beyond simulation and into experimental testing. With the Cal Poly UAV FLOC Project, such an infrastructure was created, paving the way for future experimentation with multiple UAV control systems. The control system architecture presented was built on concepts developed in previous work by Cal Poly faculty and graduate students. An outer-loop formation flight controller based on a virtual waypoint implementation of potential function guidance was developed for use on an embedded microcontroller. A commercially-available autopilot system, designed for fully autonomous waypoint navigation utilizing low cost hardware and open source software, was modified to include the formation flight controller and an inter-UAV communication network. A hardware-in-the-loop (HIL) simulation was set up for multiple UAV testing and was utilized to verify the functionality of the modified autopilot system. HIL simulation results demonstrated leader-follower formation convergence to 15 meters as well as formation flight with three UAVs. Several sets of flight tests were conducted, demonstrating a successful leader-follower formation, but with relative distance convergence only reaching a steady state value of approximately 35 +/- 5 meters away from the leader.

Intelligent transportation systems involve a variety of information and control systems methodologies, from cooperative systems which aim at traffic flow optimization by means of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, to information fusion from multiple traffic sensing modalities. This thesis aims to address three problems in intelligent transportation systems, one in optimal control of connected automated vehicles, one in discrete-event and hybrid traffic simulation model, and one in sensing and classifying roadway obstacles in smart cities. The first set of problems addressed relates to optimally controlling connected automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling. A decentralized optimal control framework is established whereby, under proper coordination among CAVs, each CAV can jointly minimize its energy consumption and travel time subject to hard safety constraints. A closed-form analytical solution is derived while taking speed, control, and safety constraints into consideration. The analytical solution of each such problem, when it exists, yields the optimal CAV acceleration/deceleration. The framework is capable of accommodating for turns and ensures the absence of collisions. In the meantime, a measurement of passenger comfort is taken into account while the vehicles make turns. In addition to the first-in-first-out (FIFO) ordering structure, the concept of dynamic resequencing is introduced which aims at further increasing the traffic throughput. This thesis also studies the impact of CAVs and shows the benefit that can be achieved by incorporating CAVs to conventional traffic. To validate the effectiveness of the proposed solution, a discrete-event and hybrid simulation framework based on SimEvents is proposed, which facilitates safety and performance evaluation of an intelligent transportation system. The traffic simulation model enables traffic study at the microscopic level, including new control algorithms for CAVs under different traffic scenarios, the event-driven aspects of transportation systems, and the effects of communication delays. The framework spans multiple toolboxes including MATLAB, Simulink, and SimEvents. In another direction, an unsupervised anomaly detection system is developed based on data collected through the Street Bump smartphone application. The system, which is built based on signal processing techniques and the concept of information entropy, is capable of generating a prioritized list of roadway obstacles, such that the higher-ranked entries are most likely to be actionable bumps (e.g., potholes) requiring immediate attention, while those lower-ranked are most likely to be nonactionable bumps(e.g., flat castings, cobblestone streets, speed bumps) for which no immediate action is needed. This system enables the City to efficiently prioritize repairs. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of the system in practice.

No description available.

As an extension of previous work done by Luke Newmeyer in his master's thesis \cite{newmeyer2018efficient}, this report presents an improved signal processing chain for efficient, real-time processing of radar data for small-scale UAV traffic control systems. The HDL design described is for a 16-channel, 2-dimensional phased array feed processing chain and includes mean subtraction, windowing, FIR filtering, decimation, spectral estimation via FFT, cross-correlation, and averaging, as well as a significant amount of control and configuration logic. The design runs near the the max allowable memory bus frequency at 300MHz, and using AXI DMA engines can achieve throughput of 38.3 Gb/s (~0.25% below theoretical 38.4 Gb/s), transferring 2MB of correlation data in about 440us. This allows for a pulse repetition frequency of nearly 2kHz, in contrast to 454Hz from the previous design. The design targets the Avnet UltraZed-EV MPSoC board, which boots custom PetaLinux images. API code and post-processing algorithms run in this environment to interface with the FPGA control registers and further process frames of data. Primary configuration options include variable sample rate, window coefficients, FIR filter coefficients, chirp length, pulse repetition interval, decimation factor, number of averaged frames, error monitoring, three DMA sampling points, and DMA ring buffer transfers. The result is a dynamic, high-speed, small-scale design which can process 16 parallel channels of data in real time for 3-dimensional detection of local UAV traffic at a range of 1000m.

No description available.

No description available.