@mastersthesis{Gonzalez2020,

title = {Rooftop-Place Suitability Analysis for Urban Air Mobility Hubs: A GIS and Neural Network Approach},

author = {Carlos Javier Delgado Gonzalez },

editor = {Joel Dinis Baptista Ferreira da Silva and Roberto Henriques and Carlos Granell-Canut 

},

url = {http://hdl.handle.net/10362/93642},

year  = {2020},

date = {2020-02-27},

address = {Lisboa},

school = {Universidade Nova De Lisboa},

abstract = {Nowadays, constant overpopulation and urban expansion in cities worldwide have led to several transport-related challenges. Traffic congestion, long commuting, parking difficulties, automobile dependence, high infrastructure maintenance costs, poor public transportation, and loss of public space are some of the problems that afflict major metropolitan areas. Trying to provide a solution for the future inner-city transportation, several companies have worked in recent years to design aircraft prototypes that base their technology on current UAVs. Therefore, vehicles with electrical Vertical Take-Off and Landing (eVTOL) technology are rapidly emerging so that they can be included in the Urban Air Mobility (UAM) system. For this to become a reality, space agencies, governments and academics are generating concepts and recommendations to be considered a safe means of transportation for citizens. However, one of the most relevant points for this future implementation is the suitable location of the potential UAM hubs within the metropolitan areas. Since although UAM vehicles can take advantage of infrastructure such as roofs of buildings to clear and land, several criteria must be considered to find the ideal location. As a solution, this thesis seeks to carry out an integral rooftop-place suitability analysis by involving both the essential variables of the urban ecosystem and the adequate rooftop surfaces for UAM operability. The study area selected for this research is Manhattan (New York, U.S), which is the most densely populated metropolitan area of one of the megacities in the world. The applied methodology has an unsupervised-data-driving and GIS-based approach, which is covered in three sections. The first part is responsible for analyzing the suitability of place when evaluating spatial patterns given by the application of Self-Organizing Maps on the urban ecosystem variables attached to the city census blocks. The second part is based on the development of an algorithm in Python for both the evaluation of the flatness of the roof surfaces and the definition of the UAM platform type suitable for its settlement. The final stage performs a combined analysis of the suitability indexes generated for the development of UAM hubs. Results reflect that 16% of the roofs in the study area have high integral suitability for the development of UAM hubs, where UAVs platforms and Vertistops (small size platforms) are the types that can be the most settled in Manhattan. The reproducibility self-assessment of this research when considering Nüst et al. [45] criteria (https://osf.io/j97zp/) is: 2, 1, 2, 1, 1 (input data, preprocessing, methods, computational environment, results). GitHub repository code is available in https://github.com/carlosjdelgadonovaims/rooftop-place_suitability_analysis_for_Urban_Air_Mobility_hubs},

keywords = {Geographic Information Systems (GIS), Mastergeotech, neural networks, urban mobility},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{Hildemann2020,

title = {3D flight route optimization for air-taxis in urban areas with evolutionary algorithms},

author = {Moritz Jan Hildemann},

editor = {Judith Verstegen and Carlos Granell-Canut and Mauro Castelli (supervisors) 



},

url = {http://hdl.handle.net/10362/94400},

year  = {2020},

date = {2020-01-31},

address = {Münster},

school = {IFGI},

abstract = {Electric aviation is being developed as a new mode of transportation for the urban areas of the future. This requires an urban air space management that considers these aircraft and restricts the vehicles’ flight routes from passing nofly areas. Flight routes need to be determined that avoid the no-fly areas and are also optimally planned in regard to minimize the flight time, energy consumption and added noise. The no-fly areas and the flight routes can be best modelled as three-dimensional geographical objects. The problem of finding a good flight route that suits all three criteria is hard and requires an optimization technique. Yet, no study exists for optimizing 3D-routes that are represented as geographical objects while avoiding three-dimensional restricted areas. The research gap is overcome by optimizing the 3D-routes with the multi-criteria optimization technique called Nondominated Sorting Genetic Algorithm (II).We applied the optimization on the study area of Manhattan (New York City) and for two representatives of different electrical aircraft, the Lilium Jet and the Ehang 184. Special procedures are proposed in the optimization process to incorporate the chosen geographical representations. We included a seeding procedure for initializing the first flight routes, repair methods for invalid flight routes and a mutation technique that relocates points along a sine curve. The resulting flight routes are compromise solutions for the criteria flight time, energy emission and added noise. Compared to a least distance path, the optimized flight routeswere improved for all three objectives. The lowest observed improvementwas a noise reduction by 36% for the Ehang 184. The highest improvement was an energy consumption reduction by 90% for the Lilium Jet. The proposed representation caused high computation times, which lead to other limitations, e.g. a missing uncertainty analysis.With the proposed methods, we achieved to optimize 3D-routes with multiple objectives and constraints. A reproducibility self-assessment1 resulted in 2, 2, 2, 2, 1 (input data, preprocessing, methods, computational environment, results).},

keywords = {3D, Geographic Information Systems (GIS), Mastergeotech},

pubstate = {published},

tppubtype = {mastersthesis}

}