Monitoramento e avalia??o de condi??es adversas em ciclovias atrav?s de multi-sensoriamento por bicicletas e processamento de dados urbanos

• Ano de defesa: 2021
• Autor(a) principal: Oliveira, Franklin L?zaro Santos de
• Orientador(a): Costa, Daniel Gouveia
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual de Feira de Santana
• Programa de Pós-Graduação: Programa de P?s-Gradu??o em Ci?ncia da Computa??o
• Departamento: DEPARTAMENTO DE TECNOLOGIA
• País: Brasil
• Palavras-chave em Português: Ciclismo inteligente; Sa?de dos ciclistas; Cidades inteligentes; Internet das coisas; Monitoramento ambiental; Redes de sensores
• Palavras-chave em Inglês: Smart cycling; Cyclists? health; Smart cities; Internet of things; Environmental monitoring; Sensor networks
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.uefs.br:8080/handle/tede/1388

Investments in mobility and sustainable transport are a trend in large cities, mainly due to the fact that traditional vehicles are expensive, inefficient in transporting people, and large polluters. At the same time, people have become more likely to seek for cleaner and healthier options for medium and short distance travel, which has also boosted the adoption of bicycles in urban areas. Faced with these changes in the behavior of the population, government agencies have been paying attention to issues of basic infrastructure, such as the construction of bike paths and the reorganization of the road network, in order to allow cyclists to travel safely. However, in order to assess the quality of cycle paths that is perceived by cyclists, a comprehensive mechanism must be implemented that incorporates different factors that can negatively impact the safety and health of cyclists, such as air pollution, risk of accidents, noise pollution, excessive sun exposure, low humidity, among others. In this context, this work proposes a new solution for monitoring, evaluation and visualization of the quality of cycle paths in order to inform cyclists about the conditions of the paths and guide governments to improve and implement new cycle paths, employing for that embedded sensing technologies, distributed data processing functions, fuzzy-based quality assessment, and a specialized networking architecture. This comprehensive multi-level solution is carefully specified and simulated on real scenarios, providing promising results that can better support the adoption of sustainable cycling in modern cities.