Predictive Algorithms Analysis to Improve Sustainable Mobility

Oscar Dario León-Granizo - University of Guayaquil, Guayaquil, Ecuador
Miguel Botto-Tobar - Eindhoven University of Technology, The Netherlands Research Group in Artificial Intelligence and Information Technology, University of Guayaquil, Ecuador

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The work is based on carrying out a comparative analysis of 3 prediction algorithms (Linear Regression, Neural Networks, and KNN), which allow the study of information on georeferential coordinates of moving objects, since through an exhaustive study it will be possible to know the predictions of each one. of them and then proceed to comply with the main objective that is to implement the algorithm with greater accuracy and effectiveness, making use of open Source tools that allow working with Machine Learning and thus be able to analyze the forecasts of traffic congestion that is formed in the surroundings of the University of Guayaquil, because this generates a great inconvenience for students and administrative personnel who belong to this institution and diminish an improvement in sustainable mobility. The methodology used is the Waterfall methodology, as it is a linear model of simple implementation, where each phase of the project was emphasized, allowing possible disorientation of the results to be managed and achieving the development of the proposed project without any inconvenience.


Analysis; algorithms; mobility; open source; prediction.

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