Decision support dashboard for traffic and environment analysis of a smart city

• Autor(a) principal: Pereira, Jorge Miguel Antunes
• Data de Publicação: 2017
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/23579
• topic: Tráfego urbano - Porto (Portugal); Redes de telecomunicações; Redes de sensores sem fios

Cities are continuously growing in population, vehicles, infrastructures and intelligence. Using and deploying smart technologies in the cities infrastructure can improve the multiple existing areas of a city, such as mobility by improving the road network, infrastructure by improving the urban planning and population by contributing with better services. Porto city has an in-place infrastructure of xed and moving sensors in more than 400 buses and roadside units, with both GPS and mobility sensors in moving elements, and with environmental sensors in xed units. This infrastructure can provide valuable data that can extract information to better understand the city and, eventually, support actions to improve the city mobility, urban planning, and environment. This work has the objective of using the information generated by the sensors placed in the buses of Porto, and using it to analyze the road tra c information based on the mobility patterns of the buses. The data from the environmental sensors deployed in Porto is also provided and used to analyze the air quality of the city and its in uence by the tra c. The developed system provides a full stack integration of the information into a city dashboard that displays and correlates the data generated from the buses movement and the environment from the xed sensors, allowing di erent visualizations over the road tra c and the environment in the city, and decisions over the current status of the city. A good example is the relation of bus speed variation with possible anomalies on the road or tra c jams. Visualizing such information with a superior level of detail on the road tra c, more anomalies can be found, adding more value to a city manager when taking urban planning decisions to improve the city mobility in a smart way.