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Location tracking is an essential problem for mobility-based applications that facilitate the daily life of Smartphone users. Existing applications often use energyhungry sensors like GPS or gyroscope to detect significant journeys. Recent research has often focused on optimizing energy consumption. As a result, approaches were proposed using sensors fusions, hybrid or eventual sensors selection. However, such research largely neglects the performance in underground tracking of automotive mobility. Possible solutions, such as those involving barometers, have well-known issues regarding performance. Oppositely, although energy-friendly, accelerometers are often overlooked based on the assumption that pattern extraction is hard due to over-noisy characteristics of the signal. In this paper, we propose a ready-to-use Framework for underground train tracking. This Framework uses an adaptive Singular Spectrum Analysis (SSA) to process the Accelerometer data. We run an empirical study using data collected from Smartphone embedded accelerometers, to track departings and arrivals of the trains in four large European cities. Results show that: 1) the Framework is able to accurately locate the trains; 2) SSA adds improvements compared to Butterworth filters and complementary filter with sensors fusion.

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.