Master's thesis presents design of public transport stops at Kounicova street in the area of the junction with the Hrnčířská street. There are important places of intersts without a direct connection to public transport. Included are three designs that were proposed depending on local conditions. Another part is the vision of a new layout Björnsen's orchard with a little precious trees and unpaved footpath, which is located in the center of academic institutions and therefore should perform representative function. The thesis also addresses the transport connection of the planned undereground garage next tu Björnsen's park.

Geografiska informationssystems användning blir allt viktigare i dagens samhälle där spatiala data kan lagras, hämtas, analyseras och visualiseras. Genom att sammanställa spatiala data kan en bild av verkligheten abstraheras. Detaljerad information om vägnat och företeelser (refuger, bullerplank, skyltar etcetera) för analys leder till ett effektivare drift- och underhållsarbete. Vilket i sin tur ger en ökad framkomlighet för trafikanter. Teknikföretaget Triona har en kartapplikation där utmaningar har uppstått gällande algoritmisk knytning av inmätta refuger (benämnd Norge-datasamlingen) till det norska vägnatet. En refug ar en upphöjning i gatan som avgränsar körfalt och påminner om en trottoar i utseendet. Denna fallstudie behandlade ett delproblem där klassificering av refuger skulle kunna underlätta knytningen och förutsättningarna for analys. Syftet med studien kan sammanfattas till att presentera förslag på metoder for att klassificera refugerna med övervakad maskininlärning. Med algoritmerna K-nearest neighbors (KNN) och Decision tree studerades möjligheten att automatiskt klassificera refugerna. En refug bestod av en vektorpolygon vilket är en lista med koordinater. Polygonens hörn bestod av koordinatparen latitud och longitud. Norge-datasamlingen var inte i forväg kategoriserad till sina elva typer och kunde därfor inte anvandas. En datasamling med 2157 refuger med sju typer från Portland, USA tillämpades i stället. De spatiala vektorpolygonerna transformerades med Elliptical Fourier Descriptors (EFD). Maskinlärningsmodellerna tränades på att klassificera refugerna baserat på matematiska approximationer av dess konturer från EFD. Slutsatser kunde dras genom att refugtypernas konturer analyserades och prestationer observerades. Prestationer utvärderades utifrån traffsäkerhet med kompletterande mätvarden som precision och återkallelse på Portland-datasamlingen. Traffsäkerhet är andelen rätta klassificeringar av refugerna. KNN uppnådde 64 % och Decisiontree 69 % traffsäkerhet. Då båda datasamlingarna var verkliga exempel på refuger i vägnat kunde ett antagande göras att det inte skulle bli en mycket högre traffsäkerhet om studiens metod appliceras på Norge-datasamlingen. Modellernas prestationer bedömdes därmed inte vara tillrackligt bra for en rekommendation. / Geographical information systems are becoming increasingly important in today´s society where spatial data can be stored, collected, analysed, and visualized. By compiling spatial data reality can be abstracted. Detailed information on road networks and objects (traffic islands, noise barriers, signs, etcetera) for analysis leads to more efficient operation and maintenance work. Which in turn provides increased accessibility for road users. The technology company Triona has a map application where algorithmic connection of traffic islands (Norway-dataset) to the Norwegian road network has been challenging. A traffic island is an elevation in the street that delimits lanes and is reminiscent of a sidewalk in appearance. This case study addressed a sub-problem where classification of traffic islands could facilitate the connection and prerequisites for analysis. The aim was to present methods that could classify the traffic islands with supervised machine learning. With the algorithms K-nearest neighbors (KNN) and Decision tree, the possibility of automatically classifying the traffic islands was studied. A traffic island consisted of a vector polygon which is a list storing its corners (latitude and longitude). The Norway-dataset was not previously labelled into its eleven types. A data collection of 2157 refuges with seven types from Portland, USA was therefore applied instead. The traffic islands were transformed with Elliptical Fourier Descriptors which extracted an approximation of its contours to train the machine learning models on. Conclusions could be drawn by analysing the contours and observing performance. Performance was evaluated based on accuracy with precision and recall on the Port-land-dataset. Accuracy is the proportion of correct classifications. KNN achieved 64% and Decision Tree 69% accuracy. As both datasets contained real traffic islands in road networks, an assumption could be made that the accuracy would not be much higher if applied on the Norway-dataset. The result was not considered sufficient for a recommendation.

The subject of my thesis is the project-level documentation for the building location on the roundabout on Vojackovo namesti in Prostejov. Current condition create light controlled crosroad of roads II/150 and III/44934. In this thesis was examinated several options and for selected option was elaborated capacity assessment. For the final shape of new designed crossroad, wich is technically elaborated Attached are the text and drawing documentation in accordance with all legal and technical standards.

The thesis in form of study discusses the possibilities of modernization of road I/11 in section between Opava and Bruntál. The need of modernization comes from fact that the road I/11 will remain in mid-term and long-term in its actual tracing and it needs to be adjusted to up-to-date requirements on road network. The thesis is based on safety inspection of road I/11 that was carried out by company Enviroad s.r.o. Proposed adjustments are divided into three stages depending on structural, economic and administrative demands. First stage introduces the least difficult adjustments that can be realized in short-term (e.g. chicanes, traffic islands, shortening of pedestrian crossings, adjustments to bus stops) and aims primary to increase road safety. Second stage presents more severe adjustments that requires greater amount of construction works but still reasonable administration level so it can be achieved in mid-term (e.g. intersection realignment, slow traffic lanes). The third stage introduces the most complex adjustments, that involves complicated administrative and construction works (all relocations of greater scale, design is based upon territorial reserves).