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Updating a robust optimization model for improving bus schedules

Transportation systems are very complex systems due to the characteristics of their components such as buses. Nowadays, buses are set up to follow a particular schedule that is very sensitive to the changes that occur inside the system. These schedules must frequently be updated, if necessary, due to many reasons. Among these reasons, we have the population growth inside the cities as well as traffic and congestions caused by unforeseen events. To solve the problem of system variability, companies such as the Public Transport Company in the city of Porto (STCP) usually fixes bus schedules with headways adapted to each type of bus lines (i.e., high/low-frequency bus lines). In this work, we adopt a robust optimization model from literature to improve the bus schedules using Automatic Vehicle Location Data collected along the year in the city of Porto. We apply the model to a high-frequency bus line case study. We present the model imperfections and propose new updates.