Abstract
Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the pervasiveness of geolocation devices and the amount of data they generate. This paper presents an approach for personal location prediction using a probabilistic model and data mining techniques over mobility data streams. We extract the individuals’ locations from relevant events in a data stream to build and maintain a Markov Chain over the important places. We evaluate the method over 3 real-world datasets. The results show the usefulness of the proposal in comparison with other well-known approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Aims. The goal of this work is to characterize the polarization effects of the beam path of the Very Large Telescope Interferometer (VLTI) and the GRAVITY beam combiner instrument. This is useful for two reasons: to calibrate polarimetric observations with GRAVITY for instrumental effects and to understand the systematic error introduced to the astrometry due to birefringence when observing targets with a significant intrinsic polarization. Methods. By combining a model of the VLTI light path and its mirrors and dedicated experimental data, we constructed a full polarization model of the VLTI Unit Telescopes (UTs) and the GRAVITY instrument. We first characterized all telescopes together to construct a universal UT calibration model for polarized targets with the VLTI. We then expanded the model to include the differential birefringence between the UTs. With this, we were able to constrain the systematic errors and the contrast loss for highly polarized targets. Results. Along with this paper, we have published a standalone Python package that can be used to calibrate the instrumental effects on polarimetric observations. This enables the community to use GRAVITY with the UTs to observe targets in a polarimetric observing mode. We demonstrate the calibration model with the Galactic Center star IRS 16C. For this source, we were able to constrain the polarization degree to within 0.4% and the polarization angle to within 5 degrees while being consistent with the literature values. Furthermore, we show that there is no significant contrast loss, even if the science and fringe-tracker targets have significantly different polarization, and we determine that the phase error in such an observation is smaller than 1 degrees, corresponding to an astrometric error of 10 mu as. Conclusions. With this work, we enable the use by the community of the polarimetric mode with GRAVITY/UTs and outline the steps necessary to observe and calibrate polarized targets with GRAVITY. We demonstrate that it is possible to measure the intrinsic polarization of astrophysical sources with high precision and that polarization effects do not limit astrometric observations of polarized targets.

Abstract
[No abstract available]

Abstract
The aim of this work is to determine, based on a market research, the level of passenger satisfaction with public transport services, in order to support better marketing decisions. This survey involves dimensions such as the level of satisfaction with timetables and frequency, vehicle conditions, driver attitudes and behavior, fares and information made available to passengers. The study was applied to the case of public transport in the Porto Metropolitan Area, Portugal, and aims to help define recommendations to improve the quality of service and define more effective marketing strategies.

Abstract
This special section features extended versions of papers originally published in the 2022 IEEE Vehicle Power and Propulsion Conference (VPPC22), hosted by the University of California, Merced, USA. This was the first time that the VPPC took place in California, USA. It was a timely visit. California recently announced that only zero-emission vehicles (ZEVs) will be allowed to be sold in the state by 2035. Other states and countries will surely follow. The VPPC, as one of the pioneer forums dedicated to electric mobility, is in a privileged position to create and disseminate knowledge that will help our communities transition toward sustainable transportation, improving air quality and reducing greenhouse emissions.

Abstract
Reducing CO(2 )emissions is currently a key policy in most developed countries. In this article, we evaluate whether smart traffic lights can have a relevant role in reducing CO2 emissions in small cities, considering their specific traffic profiles. The research method is a quantitative modelling approach tested by computational simulation. We propose a novel microscopic traffic simulation framework, designed to simulate realistic vehicle kinematics and driver behaviour, and accurately estimate CO(2 )emissions. We also propose and evaluate a routing algorithm for smart traffic lights, specially designed to optimize CO(2 )emissions at intersections. The simulations reveal that deploying smart traffic lights at a single intersection can reduce CO2 emissions by 32% to 40% in the vicinity of the intersection, depending on the traffic density. The simulations show other advantages for drivers: an increase in average speed of 60% to 101% and a reduction in waiting time of 53% to 95%. These findings can be useful for city-level decision makers who wish to adopt smart technologies to improve traffic flows and reduce CO2 emissions. This work also demonstrates that the simulator can play an important role as a tool to study the impact of smart traffic lights and foster the improvement in smart routing algorithms to reduce CO2 emissions.