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Analysis of the factors generating vehicular traffic in the city of Quito and its relation to the application of sensorial and social data with big data as a basis for decision making

Abstract:

This research corresponds to a first phase of study on the search of the main causes that generate vehicular traffic in the Metropolitan District of Quito and approaching potential application I.3 through the use of intelligent systems based on social networks and sensorial data using Big Data. So the current situation of the city is analyzed as well as the problem that traffic represent for Quito, subsequently the analysis of various cases throughout the world who have suffered the problem of traffic congestion is summarized and then is analized and related with solutions that have been taken and which have improved mobility in these cities. While the focus of this study is not to provide direct solutions to the problem of transportation but rather to present the diagnosis of the main problems. The study deepens with field research with 95% confidence to a total of 384 people by random cluster sampling, seeking to make a representative study of the population that is transported in Quito through a poll. The results determined that the population considers congestion as one of the main problems of the city. Meanwhile in research the perception of the population is presented about the various problems that generate traffic in the city. The vehicular increase and the lack of public transport use are analyzed as part of the results and finally the feasibility of using tools such as social networking identified as potencial tools to capture information that allows analyzing alternative solutions to traffic congestion. The study concludes with an analysis of both the main problems and potential solutions to improve traffic and the feasible use of social network Twitter as a tool for decision making for better efficiency of traffic by better use of routes by transportation means as vehicles, achieving research with an initial analysis, but critical to the implementation of potential solutions for decision making in improving traffic congestion in the city.