Microservicios aplicados a la integración y análisis de datos orientados al cálculo de una tarifa diferencial para aparcaderos de vehículos privados. Caso de estudio: Campus Central de la Universidad de Cuenca

Abstract

The use of motorized modes in Cuenca, Ecuador, has increased in the last decade, contributing to traffic congestion in its central area. To meet this growing demand, the supply of parking lots that operate independently without regularization on the rates to be charged is increased. Thus, in the city, parking a vehicle is cheap, which has facilitated its massification. To avoid the negative consequences associated with motorized mobility, it is convenient to define a rate plan to influence the mode choice. The aim of this study is to implement the mathematical model proposed by the Active and Sustainable Mobility Group of the University of Cuenca defining a differential rate plan for the use of parking with a case study of the Central Campus of the University of Cuenca. The methods used are Hefesto, CrispDM and Tariff Model: the first one used in the construction of a Data Warehouse to consolidate the data collected in the campus parking lots and visualize indicators of interest through information cubes; the second followed an orderly process cycle for the extraction of knowledge from the Data Warehouse through Data Mining techniques; finally, the mathematical model was validated by comparing personalized rates for university servers. The materials used were: Tariff Model, Administrative and Mobility Data; and, among the technologies used are: Pentaho Suite, Spring Boot, among others. The results obtained include a Data Warehouse to answer questions of interest, in addition to being a data source to apply Data Mining with its consequent Artificial Intelligence models, and the creation of a microservice to calculate the personalized rate through the model, which is used by a web application available to the mobility group. Finally, it is concluded by analyzing the use of the Datawarehouse, and observing its impact on the model; and the rate that each user would have, based on the central objectives of the collection rate.