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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.

Simulation is a computer-based experimentation tool suitable to determine the efficacy of a previously untried decision. In this paper, we present a model of climate change. The goal behind this project is to provide a test-bed to evaluate theories related to the Earth system so as to test and evaluate metrics such as greenhouse gases and climate change in general. The proposed approach is based on a multi-agent model which has as input a representation of nature and as output the changes that will occur on Earth within a given instant of time. Most views about climate change do not take into account the real severity of the subject matter; however, the present perspective is given in a way so as to make non-experts aware of the risks that are threatening life on Earth. Just recently, the general population has developed considerable sensitivity to these issues. One important contribution of this work is to use agent-based modeling and simulation as an instructional tool that will allow people to easily understand all aspects involved in the preservation of the environment in a more aware and responsible way.