Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach

Detalhes bibliográficos
Autor(a) principal: Delgado Gonzalez, Carlos Javier
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/93642
Resumo: Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
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spelling Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approachArtificial Neural NetworkCatchment AreaCensus BlockClusterDriving DistanceElectrical Vertical Take-off and LandingFlatnessGeographical SOMK-meansLight Detection and RangingMachine LearningNeuronParallel ProcessingPoints-Of-InterestU-MatrixRooftopSelf-Organizing MapsSuitability AnalysisU-MatrixUnmanned AerialVertihubVertiport KDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesNowadays, 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_hubsSilva, Joel Dinis Baptista Ferreira daHenriques, Roberto André PereiraGranell-Canut, CarlosRUNDelgado Gonzalez, Carlos Javier2020-03-02T17:59:33Z2020-02-272020-02-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/93642TID:202456889enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:41:56Zoai:run.unl.pt:10362/93642Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:48.241009Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach
title Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach
spellingShingle Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach
Delgado Gonzalez, Carlos Javier
Artificial Neural Network
Catchment Area
Census Block
Cluster
Driving Distance
Electrical Vertical Take-off and Landing
Flatness
Geographical SOM
K-means
Light Detection and Ranging
Machine Learning
Neuron
Parallel Processing
Points-Of-Interest
U-Matrix
Rooftop
Self-Organizing Maps
Suitability Analysis
U-Matrix
Unmanned Aerial
Vertihub
Vertiport K
title_short Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach
title_full Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach
title_fullStr Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach
title_full_unstemmed Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach
title_sort Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach
author Delgado Gonzalez, Carlos Javier
author_facet Delgado Gonzalez, Carlos Javier
author_role author
dc.contributor.none.fl_str_mv Silva, Joel Dinis Baptista Ferreira da
Henriques, Roberto André Pereira
Granell-Canut, Carlos
RUN
dc.contributor.author.fl_str_mv Delgado Gonzalez, Carlos Javier
dc.subject.por.fl_str_mv Artificial Neural Network
Catchment Area
Census Block
Cluster
Driving Distance
Electrical Vertical Take-off and Landing
Flatness
Geographical SOM
K-means
Light Detection and Ranging
Machine Learning
Neuron
Parallel Processing
Points-Of-Interest
U-Matrix
Rooftop
Self-Organizing Maps
Suitability Analysis
U-Matrix
Unmanned Aerial
Vertihub
Vertiport K
topic Artificial Neural Network
Catchment Area
Census Block
Cluster
Driving Distance
Electrical Vertical Take-off and Landing
Flatness
Geographical SOM
K-means
Light Detection and Ranging
Machine Learning
Neuron
Parallel Processing
Points-Of-Interest
U-Matrix
Rooftop
Self-Organizing Maps
Suitability Analysis
U-Matrix
Unmanned Aerial
Vertihub
Vertiport K
description Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
publishDate 2020
dc.date.none.fl_str_mv 2020-03-02T17:59:33Z
2020-02-27
2020-02-27T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/93642
TID:202456889
url http://hdl.handle.net/10362/93642
identifier_str_mv TID:202456889
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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