The widespread use of digital technologies in society enables new and more efficient ways to operate a few types of systems. In the case of urban mass transportation systems (metro), the digital technologies make it possible to obtain information on the users’ behavior. Therefore, making use of these data, it is theoretically possible to forecast how, when and where passengers are to get in and out of the system facilities. However, the use of forecasting techniques to improve operation efficiency or the relationship with travelers in metro systems is not commonplace yet. In this thesis, we propose to develop, based on how travelers get in and out stations over time, a model to accurately forecast the dynamic evolution of system origin-destination matrices. With this information, a data-driven method that leads to changes in the metro system’s state derived from online measurements. In addition, we will explore new possible ways of enhancing the way to interact with and provide services to the system users.
Requirements: MSc in Industrial Engineering, MSc in Telecommunication Engineering, MSc in Big Data and Advanced Technology. Good command of advanced analytics and machine learning techniques.
Full-time contract with exclusive dedication to the PhD thesis.
Documents: Curriculum vitae, academic record, cover letter and two recommendation letters.
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