Diploma thesis is the project of a six-storey multifunctional building in Brno. The building is designed for commercial and housing. In the basement is a garage for residents with a technical room, first floor are commercial spaces such as cafes, bike shop, newspaper shop, bakery, then there is the entrance to do the apartments which are located in the remaining upper floors. Each residential floor has 6 apartments, 4 three-room and 2 four-room apartments. There is a total of 24 apartments. Housing has green flat roof, above the underground is operational flat roof with a parking area. This project is developed with a computer software AutoCAD, 3D models are developed with ArchiCAD and renders are developed with rendering software Lumion. This thesis was created with an emphasis on a correct layout and architectural design, static requirements and safety construction usage.

Die LH München wurde 2010 mit dem Wunsch von CarSharing-Unternehmen konfrontiert, eine Ausnahmegenehmigung für das Parken in Parklizenzgebieten zu gewähren. Eine Ausnahmegenehmigung ist grundsätzlich nur dann zu rechtfertigen, wenn sie im öffentlichen Interesse ist. Das öffentliche Interesse hat die LH München dann als gegeben definiert, wenn durch die Nutzung der Carsharing-Fahrzeuge mehr Stellplätze frei gemacht würden, als sie selbst benötigen und die gefahrenen Fahrzeugkilometer in der Stadt in der Gesamtsumme nicht zunehmen würden. Der Projektbericht beschreibt Methodik und Ergebnisse einer Evaluation des Münchner Carsharing-Angebotes. Dieser lagen im Wesentlichen Daten aus drei Quellen zugrunde: Daten aus bereits zum Thema vorliegenden Studien sowie vorliegende Kennzahlen, Daten zu Nutzern und Nutzung aus den Back-End-Systemen der Carsharing-Anbieter und Daten aus eigenen Befragungen.

Radar map generation using binary Bayes filter or what is commonly known as Inverse Sensor Model; which translates the sensor measurements into grid cells occupancy estimation, is a classical problem in different fields. In this work, the focus will be on development of Inverse Sensor Model for parking space using 77 GHz FMCW (Frequency Modulated Continuous Wave) automotive radar, that can handle different environment geometrical complexity in a parking space. There are two main types of Inverse Sensor Models, where each has its own assumption about the sensor noise. One that is fixed and is similar to a lookup table, and constructed based on combination of sensor-specific characteristics, experimental data and empirically-determined parameters. The other one is learned by using ground truth labeling of the grid map cell, to capture the desired Inverse Sensor Model. In this work a new Inverse Sensor Model is proposed, that make use of the computational advantage of using fixed Inverse Sensor Model and capturing desired occupancy estimation based on ground truth labeling. A derivation of the occupancy grid mapping problem using binary Bayes filtering would be performed from the well known SLAM (Simultaneous Localization and Mapping) problem, followed by presenting the Adaptive Inverse Sensor Model, that uses fixed occupancy estimation but with adaptive occupancy shape estimation based on statistical analysis of the radar measurements distribution across the acquisition environment. A prestudy of the noise nature of the radar used in this work is performed, to have a common Inverse Sensor Model as a benchmark. Then the drawbacks of such Inverse Sensor Model would be addressed as sub steps of Adaptive Inverse Sensor Model, to be able to haven an optimal grid map occupancy estimator. Finally a comparison between the generated maps using the benchmark and the adaptive Inverse Sensor Model will take place, to show that under the fulfillment of the assumptions of the Adaptive Inverse Sensor Model, the Adaptive Inverse Sensor Model can offer a better visual appealing map to that of the benchmark.

Inductive charging, a form of wireless charging, uses an electromagnetic field to transfer energy between two objects. This emerging technology offers an alternative solution to users having to physically plug in their electric vehicle (EV) to charge. Whilst manufacturers claim inductive charging technology is market ready, the efficiency of transfer of electrical energy is highly reliant on the accurate alignment of the coils involved. Therefore understanding the issue of parking misalignment and driver behaviour is an important human factors question, and the focus of this paper. Two studies were conducted, one a retrospective analysis of 100 pre-parked vehicles, the second a dynamic study where 10 participants parked an EV aiming to align with a charging pad with no bay markings as guidance. Results from both studies suggest that drivers are more accurate at parking laterally than in the longitudinal direction, with a mean lateral distance from the centre of the bay being 12.12 and 9.57 cm (retrospective and dynamic studies respectively) compared to longitudinally 23.73 and 73.48 cm. With current inductive charging systems having typical tolerances of approximately ±10 cm from their centre point, this study has shown that only 5% of vehicles in both studies would be aligned sufficiently accurately to allow efficient transfer of electrical energy through induction.

L’objectif de cette thèse est de développer, d’intégrer et de tester une nouvelle approche algorithmique d’aide au stationnement dans les centres urbains. Considérons différents types d’infrastructures déployées allant de la détection des entrées/sorties des véhicules jusqu'à la variation dans le temps du nombre de places de stationnement disponibles dans chaque portion de rue, nous montrons qu’il est possible de proposer une méthode efficace qui détermine un itinéraire qui minimise l’espérance de temps pour trouver une place de stationnement disponible et de prédire la disponibilité des placesde stationnement.Pour cela, la zone urbaine choisie sera donc considérée comme un ensemble de ressources de stationnement (segments de rues).Nous modélisons d’abord cette zone urbaine par un graphe où les sommets désignent les carrefours et les arcs représentent les portions de rues. Les paramètres essentiels pour notre modèle de réseau urbain sont la capacité de stationnement et le temps de parcours des portions de rue.L’originalité et l’aspect innovant de notre approche s’appuient sur deux principes.Le premier principe concerne le guidage comme une ressource : il ne s’agit pas de guider vers une place libre mais de proposer un parcours qui optimise l’espérance de temps de trouver une telle place. Pour cela nous déterminons dans une zone centrée sur une destination donnée, le parcours à effectuer par un véhicule pour minimiser son espérance de temps de trouver une place destationnement le plus rapidement possible.Ainsi nous avons mis en œuvre un algorithme d’apprentissage par renforcement basée sur la méthode LRI (Linear Reward Inaction) et la méthode Monte Carlo pour minimiser l’espérance de temps de trouver une place de stationnement en zone urbaine.Nous avons comparé cet algorithme avec une approche globale basée sur l’évaluation arborescente à profondeur bornée.Le second principe repose sur la prédiction des places de stationnement disponibles par périodes de temps homogènes où on ne s’intéresse pas à une place de stationnement en temps réel mais aux places de stationnement par zones. Il s’agit alors pour le système de pouvoir prédire le potentiel de places libres dans chacune des ressources pour les prochaines périodes. On ne vise donc pas ici la prédiction de la disponibilité de chaque place ; chaque ressource sera considérée comme une zone de stockage dont la disponibilité sera établie en grande partie en fonction des flux d’entrée et de sortie de la portion. Pour ce principe, nous avons donc déterminé par algorithmes de calculs et d’apprentissages la probabilité qu’il y ait au moins une place libre pour stationner dans un tronçon de rue pour un créneau de temps donné. Les principales données nécessaires pour effectuer ces calculs sont les séries temporelles d’entrée sortie de chaque véhicule aux intersections des rues et les variations des places de stationnement au cours du temps.Nous avons évalué les performances de notre approche par simulations sur des données générées aléatoirement et des données réelles obtenues sur un quartier de Versailles. / The objective of this thesis is to develop, to integrate and to test a new algorithmic approach to help parking in urban centers.Given the different types of deployed infrastructure : from input-output detection of vehicles to time variation of the number of available places within each street segment, we propose an efficient method to determine an itinerary that minimize the time expectation to find an available place and also to predict the availability of the parking places.We have chosen an urban area and we have considered it as a set of parking resources called street segments. More exactly, this urban area is considered as a graph where the vertexes represent the crossroads and the arcs represent the street segments. The essential parameters of our urban area model are the parking capacity and the time crossing of each street segment. The originality and the innovation of our approach are based on two principles.The first one is the guidance as a resource, i.e., it means that the proposed itinerary is not the one that lead to an available parking place but rather the one that minimized the time expectation to find an available parking place. In order to achieve that we determine, in a an area centered on a given destination, the itinerary to follow by the vehicle in order minimize its time expectation to find an available parking place as quickly aspossible.We have designed and realized a reinforcement learning algorithm based on the LRI method (Linear Reward Inaction) and a Monte Carlo method to minimize the time expectation to find an available parking place in the urban area. We have compared this algorithm to a global approach based on tree evaluation with bounded depth. The second principle is based on the prediction of the parking places by homogeneous time period where we are not interestedon a parking place in real time but rather on the parking places byarea. In other terms, the system predict the potential available parkingplaces by resource for the next time periods. Thus, we don’t aim to predict the availability of each parking place, i.e., each resource is considered as stock area and its availability is assessed in major part in function of the street segment input-output flow. For this principle, we have determined by a learning algorithm the probability that there is at least one available parking place in a street segment within a given time. The major data needed to compute this probability are the time series of input-output of each vehicle in street intersections, and the variation of the available parking places through the time.We have evaluated the performance of this approach by simulation based on random generated data and on real data of a district in Versailles.

This diploma thesis focuses on the transport analysis in the town Červený Kostelec in the Hradec Králové Region. The work contains analysis of transport modes, especially road transport and parking, concurrently proposes their optimization. The first part of the thesis provides basic informations of the town Červený Kostelec. Next there is described the current traffic situation in the town. The second part of the work focuses on the analysis and solution of the difficult places.

Transportsektorns utsläpp av växthusgaser står för ungefär en tredjedel av de totala utsläppen i Sverige. Till 2030 ska dessa minska med 70% jämfört med 2010 års nivåer. Utöver växthusgaser ger biltransporter upphov till trängsel och utsläpp av partiklar och gaser som påverkar hälsa och miljö negativt, särskilt i tät i stadsmiljö. På global, såväl som nationell nivå finns mål som strävar efter att förbättra miljön och inte sällan lyfts kollektivtrafik, gång och cykel fram som hållbara transportslag för stadsmiljö.  Studien Södertälje, cykel, city har sitt ursprung i Södertälje kommuns ambition att bli en bättre stad för cyklister. Idag konstateras att andelen resor som sker med cykel är låg trots stadens goda förutsättningar för cyklism. Ett mål i den kommunala cykelplanen är att understödja fler cykelresor med hjälp av förbättrad cykelinfrastruktur. Cykelinfrastrukturen innefattar flera delar och den här studien fokuserar på cykelparkering då brist på säker cykelparkering är en vanlig orsak till att södertäljebor avstår från att cykla. Med kartläggning av nuvarande cykelparkeringssituationen syftar denna studie till att producera kunskap till hjälp i planeringen av stadens framtida cykelparkering.  Med hjälp av litteratur på området, insamlad empirisk data och GIS-teknik visualiseras och analyseras hur stadskärnans cyklister parkerar och utformningen av stadskärnans cykelparkeringar. Av studiens resultat framgår att ungefär hälften av cyklarna inom undersökningsområdet parkeras utanför anordnad parkering och att det generellt sett inte råder någon kapacitetsbrist på cykelparkeringarna. Av de cyklar som parkerats utanför ställ återfinns 65% längre bort än 25 meter ifrån anordnade parkering. En av studiens slutsatser är att närhet till cykelparkering inom undersökningsområdet är bristfällig och att andra typer av cykelinfrastruktur är mer adekvat för att nå uppsatta mål om fler resor på cykel. / The transport sector's emissions of greenhouse gases account for about a third of the total emissions in Sweden, by the year 2030 these shall decrease by 70% compared to 2010 levels. In addition to greenhouse gases, car transport contributes to congestion and emissions of particles and gases that negatively affect health and the environment, especially in dense urban environments. At global, national and local levels there are goals in order to improve the environment which often highlight public transport, walking and cycling as sustainable modes of transport in the urban environment. The study Södertälje, cycle, city has its origins in the municipality of Södertälje’s ambition to become a better city for cyclists. Today, the municipality considers the proportion of trips made by bicycle is low even though the city has good conditions to incubate cycling. A designated tool in the municipal cycle plan is to support more cycle journeys with the use of an improved bicycling infrastructure. Organised bicycle parking is part of the bicycle infrastructure that can provide users a number of benefits. At the same time there is an inherent advantage of the bicycle which is its flexibility in terms of parking. Due to that, this study maps and analyses the bicycle parking situation in a central Södertälje. This in order to create greater knowledge and understanding of cyclists' needs but also to support the planning of the city's future bicycle parking. With the help of literature in the field, collected empirical data and GIS technology, how the city centre cyclists park and the design of the city centre bicycle parking is visualized and analysed. The results of the study show that approximately half of the bicycles in the study area are parked outside organized parking and that there is no generall lack of capacity in the organised bicycle parking. Of the bicycles parked outside racks, 90% are found far further than 25 meters from an organized car park. One of the study's conclusions is that proximity to bicycle parking within the study area is deficient and that other types of bicycle infrastructure are more adequate to achieve set goals for more trips by bicycle.

Path planning that considers accurate vehicle dynamics and obstacle avoidance is an important problem in the area of autonomous driving. This paper describes a method of implementing trajectory planning for autonomous parking in conditions where the starting point and the position of fixed obstacles are uncertain. The narrow spaces and complicated manoeuvres required for parking demands a lot from the trajectory planning algorithm. It needs to have the ability to accurately model vehicle dynamics and find an efficient way around obstacles. Having obstacles in the way of the parking vehicle makes this a nonconvex problem the goal can usually not be reached by travelling in a straight line and finding a perfect trajectory around them is generally not computationally tractable. This paper reviews a two tiered approach to solving this problem. First a rough path is found using a modified Rapidly-exploring Random Tree (RRT) algorithm called Forward-Backward RRT, which runs two treebuilding processes in parallel and constructs a feasible path from where they intersect. Using optimisation this is then improved into a trajectory that is at least a local optimum. These methods will be demonstrated to produce efficient and feasible trajectories that respects the dynamic constraints of the vehicle and avoids collisions.

Last mile transportation is the most problematic phase of transportation needing additional research and effort. Longer waits or search times, lack of navigational directions and real-time information are some of the common problems associated with last mile transportation. Inefficient last mile transportation has an impact on the environment, fuel consumption, user satisfaction and business opportunities. Last mile problems exist in several transportation domains, such as: the landing of airplanes, docking of ships, parking of vehicles, attended home deliveries, etc. While there are dedicated inter-connected decision support systems available for ships and aircraft, similar systems are not widely utilized in parking or attended handover domains. Therefore, the scope of this thesis covers last mile transportation problems in parking and attended handover domains. One problem area for parking and attended handovers is due to lack of real-time information to the driver or consumer. The second problem area is dynamic scheduling where the handover vehicle must traverse additional distance to multiple handover locations due to lack of optimized routes. Similarly, during parking, lack of navigational directions to an empty parking space can lead to increased fuel consumption and CO2 emissions. Therefore, aim of this thesis is to design and develop decision support systems for last mile transportation problems by holistically addressing real time customer communication and dynamic scheduling problem areas. The problem areas discussed in this thesis consists of persistent issues even though they were widely discussed in the literature. In order to investigate the problem areas, microdata analysis approach was implemented in the thesis. The phases involved in Microdata analysis are: data collection, data processing, data storage, data analysis and decision-making. Other similar research domains, such as: computer science or statistics also involve phases such as data collection, processing, storage and analysis. These research domains also work in the fields of decision support systems or knowledge creation. However, knowledge creation or decision support systems is not a mandatory phase in these research domains, unlike Microdata analysis. Three papers are presented in this thesis, with two papers focusing on parking domains, while the third paper focuses on attended handover domains. The first paper identifies available smart parking tools, applications and discusses their uses and drawbacks in relation to open parking lots. The usage of cameras in identifying parking occupancy was recognized as one of the suitable tools in this paper. The second paper uses a thermal camera to collect the parking lot data, while deep learning methodologies were used to identify parking occupancy detection. Multiple deep learning networks were evaluated for identifying parking spaces and one method was considered suitable for acquiring real time parking occupancy. The acquired parking occupancy information can be communicated to the user to address real-time customer communication problems. However, the decision support system (DSS) to communicate parking occupancy information still needs to be developed. The third paper focuses on the attended handovers domain where a decision support system was reported which addresses real-time customer communication and dynamic scheduling problems holistically. Based on a survey, customers accepted the use of mobile devices for enabling a real-time information flow for improving customer satisfaction. A pilot test on vehicle routing was performed where the decision support system reduced the vehicle routing distance compared to the route taken by the driver. The three papers work in developing decision support systems for addressing major last mile transportation problems in parking and attended handover domains, thus improving customer satisfaction, and business opportunities, and reducing fuel costs, and pollution.

The thesis aims to create architectural and urban proposals for the use of selected parts of the Stare Brno.