Travel time is one of the most important inputs in many Intelligent Transport Systems (ITS). As a result, this information needs to be accurate and dynamic in both spatial and temporal dimensions. For the estimation of travel time, data from fixed sensors such as Inductive Loop Detectors (ILD) and cameras have been widely used since the 1960 s. However, data from fixed sensors may not be sufficiently reliable to estimate travel time due to a combination of limited coverage and low quality data resulting from the high cost of implementing and operating these systems. Such issues are particularly critical in the context of Less Developed Countries, where traffic levels and associated problems are increasing even more rapidly than in Europe and North America, and where there are no pre-existing traffic monitoring systems in place. As a consequence, recent developments have focused on utilising moving sensors (i.e. probe vehicles and/or people equipped with GPS: for instance, navigation and route guidance devices, mobile phones and smartphones) to provide accurate speed, positioning and timing data to estimate travel time. However, data from GPS also have errors, especially for positioning fixes in urban areas. Therefore, map-matching techniques are generally applied to match raw positioning data onto the correct road segments so as to reliably estimate link travel time. This is challenging because most current map-matching methods are suitable for high frequency GPS positioning data (e.g. data with 1 second interval) and may not be appropriate for low frequency data (e.g. data with 30 or 60 second intervals). Yet, many moving sensors only retain low frequency data so as to reduce the cost of data storage and transmission. The accuracy of travel time estimation using data from moving sensors also depends on a range of other factors, for instance vehicle fleet sample size (i.e. proportion of vehicles equipped with GPS); coverage of links (i.e. proportion of links on which GPS-equipped vehicles travel); GPS data sampling frequency (e.g. 3, 6, 30, 60 seconds) and time window length (e.g. 5, 10 and 15 minutes). Existing methods of estimating travel time from GPS data are not capable of simultaneously taking into account the issues related to uncertainties associated with GPS and spatial road network data; low sampling frequency; low density vehicle coverage on some roads on the network; time window length; and vehicle fleet sample size. Accordingly this research is based on the development and application of a methodology which uses GPS data to reliably estimate travel time in real-time while considering the factors including vehicle fleet sample size, data sampling frequency and time window length in the estimation process. Specifically, the purpose of this thesis was to first determine the accurate location of a vehicle travelling on a road link by applying a map-matching algorithm at a range of sampling frequencies to reduce the potential errors associated with GPS and digital road maps, for example where vehicles are sometimes assigned to the wrong road links. Secondly, four different methods have been developed to estimate link travel time based on map-matched GPS positions and speed data from low frequency data sets in three time windows lengths (i.e. 5, 10 and 15 minutes). These are based on vehicle speeds, speed limits, link distances and average speeds; initially only within the given link but subsequently in the adjacent links too. More specifically, the final method draws on weighted link travel times associated with the given and adjacent links in both spatial and temporal dimensions to estimate link travel time for the given link. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-Berkeley s Mobile Century Project. The original GPS dataset which was broadcast on a 3 second sampling frequency has been extracted at different sampling frequencies such as 6, 30, 60 and 120 seconds so as to evaluate the performance of each travel time estimation method at low sampling frequencies. The results were then validated against reference travel time data collected from 4,126 vehicles by high resolution video cameras, and these indicate that factors such as vehicle sample size, data sampling frequency, vehicle coverage on the links and time window length all influence the accuracy of link travel time estimation.

This diploma thesis deals with the proposal of a personal data protection system according to the General Data Protection Regulation in the organization Dentalife s.r.o.. The proposal was implemented on the basis of an analysis of the current situation which revealed serious shortcomings in line with the General Data Protection Regulation. Based on the identified deficiencies, a recommendation has been drawn up which, in the event of its subsequent implementation, will put the current situation into line with this Regulation. The theme of the diploma thesis was selected primarily because of its up-to-date and missing materials that would describe and explain the individual steps of the whole process of analysis and implementation.

Im Zuge der immer größer werdenden Knappheit fossiler Ressourcen und des damit verbundenen Anstiegs des Rohölpreises ergibt sich ein Trend hin zur Elektromobilität. In den nächsten Jahren werden jedoch nur Elektrofahrzeuge mit deutlich eingeschränkter maximaler Reichweite im Vergleich zu Benzin- oder Dieselfahrzeugen produziert werden können. Um den täglichen Umgang des Kunden mit einem Elektrofahrzeug trotzdem möglichst reibungslos zu ermöglichen, wurde im Rahmen dieser Arbeit eine neuartige Anzeige der verbleibenden Reichweite auf der Navigationskarte entwickelt. Nach detaillierter Analyse vorhandener Ansätze wurde je ein empirisches und ein modellbasiertes Verfahren ausgearbeitet. Die Ansätze wurden systematisch verglichen und zu einem komplett neuartigen, hybriden Ansatz kombiniert. Die auftretenden Verbräuche des Fahrzeugs werden im Kundenbetrieb erfasst, je nach Fahrsituation klassifiziert und für eine Extrapolation in der Zukunft verwendet. Die entwickelte Methodik zur Untersuchung der erreichbaren Genauigkeit ergab ein erzielbares Fehlermaß von durchschnittlich unter 10%. / Due to the prospect of a worldwide shortage of fossil fuels and the correlated increase of prices for crude-oil, a global trend to invest in electric mobility has started. During the next couple of years, electric vehicles will still have restrictions on the maximum distance that can be driven before having the need to recharge the battery. The potential costumers face the so-called „range-anxiety“, the fear to be stranded prior to reaching the destination. In order to provide a safe and easy way of operating such a vehicle, the work conducted in the course of this doctoral thesis led to a new way of displaying the remaining range of the vehicle on a navigation map. After detailed analysis of the state of the art, an empirical- and a model-based solution for calculating the remaining range were developed utilizing predictive map-data from a roadnetwork. After a systematical optimization of the developed solutions, an embedded prototype was developed which captured the driving situation of the vehicle together with the corresponding energy-consumption in order to provide a context-aware interpolation of the remaining range, depending on where the costumer would drive next. A developed methodology of objectively determining the error produced by the system resulted in a mean-deviation of 10% of absolute value.