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A procedure is developed that allows an App to predict the state of complex traffic articulated with a Google Maps API and Learning Machine libraries. For such purposes, a vehicular traffic zone is defined where the times invested by a driver when traveling these streets are estimated. Each of the examples is associated with a traffic condition in four categories: low, medium, moderate and high. In this way, a training set is created that is the information base of an artificial intelligence algorithm. A neural network of the MultiplayerPerceptron type with a single layer and 28 neurons is chosen. The classifier gave a high TP Rate and a very low FP Rate, indicating very satisfactory classification instances for the four categories. The Precision of the model shows a good sensitivity and the Memory an adequate specificity. The ROC area exceeds 0.9 in all cases, reflecting a very good test. The confusion matrix is made up of a single diagonal, showing good sensitivity and precision.

Fusión y análisis de datos para la toma de decisiones en un escenario vehicular complejo