Learning spatial inequalities: an approach to support transportation planning.

• Ano de defesa: 2018
• Autor(a) principal: Gay, Juliana Siqueira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Accessibility; Desigualdades; Equidade; Machine learning; Mobilidade urbana; Mobility plans; Planejamento de transportes; Sistemas de transportes; Transport systems; Uso do solo
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/3/3138/tde-03052018-103817/

Part of the literature of transportation planning understand transportation infrastructure as a mean of distributing people and opportunities across the territory. Therefore, the spatial inequalities become a relevant issue in transportation and land use planning. To meet the challenge of evaluating the heterogeneity of transportation provision and land use in the urban environment, this work aims at identifying and describing patterns hidden the distribution of accessibility to different urban facilities and socioeconomic information using Machine Learning (ML) techniques to inform the decision making of transportation plans. To feature the current consideration of spatial inequalities measures in the practice of transportation planning in Brazil, nine mobility plans were reviewed. For investigating the potentialities and restrictions of ML application, unsupervised and supervised analysis of income and accessibility indicators to health, education and leisure were performed. The data of the São Paulo municipality from the years of 2000 and 2010 was explored. The analyzed plans do not present measures for evaluating spatial inequalities. It is possible to identify that the low-income population has low accessibility to all facilities, especially, hospital and cultural centers. The east zone of the city presents a low-income group with intermediate level to public schools and sports centers, revealing the heterogeneity in regions out of the city center. Finally, a framework is proposed to incorporate spatial inequalities by using ML techniques in transportation plans.