Predictive Algorithms Analysis to Improve Sustainable Mobility

In this work, a comparative analysis of 3 prediction algorithms (Linear Regression, Neural Networks, and KNN) was carried out, which allows for studying georeferential coordinates of moving objects. Through an exhaustive study, it will be possible to know the predictions of each algorithm, which will make a comparison of results. This will help implement an algorithm with greater accuracy and effectiveness in a system developed as a research project called Intelligent System for Sustainable Mobility of the University of Guayaquil (SIAMS-UG), using open source tools that allow working with Machine Learning. It will be possible to analyze the forecasts of the congestions that are formed in the surroundings of the University of Guayaquil. This problem generates inconveniences for both students and administrative staff that are part of this institution. The methodology used for the project's development was the waterfall methodology, as it is a linear and simple implementation model where each phase of the project was emphasized, allowing the management of the results and the successful completion of the project.