Traces collected by citizens using GNSS (Global Navigation Satellite System) devices during sports activities such as running, hiking or biking are now widely available through different sport-oriented collaborative websites. The traces are collected by citizens for their own purposes and frequently shared with the sports community on the internet. Our research assumption is that crowdsourced GNSS traces may be a valuable source of information to detect updates in authoritative datasets. Despite their availability, the traces present some issues such as poor metadata, attribute incompleteness and heterogeneous positional accuracy. Moreover, certain parts of the traces (GNSS points composing the traces) are results of the displacements made out of the existing paths. In our context (i.e., update authoritative data) these off path GNSS points are considered as noise and should be filtered. Two types of noise are examined in this research: Points representing secondary activities (e.g., having a lunch break) and points representing errors during the acquisition. The first ones we named secondary human behaviour (SHB), whereas we named the second ones outliers. The goal of this paper is to improve the smoothness of traces by detecting and filtering both SHB and outliers. Two methods are proposed. The first one allows for the detection secondary human behaviour by analysing only traces geometry. The second one is a rule-based machine learning method that detects outliers by taking into account the intrinsic characteristics of points composing the traces, as well as the environmental conditions during traces acquisition. The proposed approaches are tested on crowdsourced GNSS traces collected in mountain areas during sports activities.

Understanding the relationship between socio-economic factors and travel behavior is crucial for developing sustainable and inclusive transportation policies. This study analyzes mode choice, daily trip frequency, and daily travel time in Switzerland using high-resolution GPS data from the TimeUse+ project, which provides detailed mobility records from over 1,000 individuals. Unlike traditional survey-based studies, GPS tracking offers more precise insights into daily mobility patterns by reducing recall bias and capturing short trips often omitted in self-reported data. Our analysis reveals clear differences between weekday and weekend travel: On weekdays, socio-demographic variables (such as gender and age) together with transport supply variables (such as car ownership and public transport subscriptions) significantly shape mobility while on weekends, these effects largely weaken, and lifestyle-driven choices dominate. Notably, adults aged 66 + are the most mobile group on weekdays in terms of trip frequency, while individuals aged 56–65 take fewer but longer trips. Women make fewer trips overall, likely due to higher load of household-related responsibilities. Households with children reduce travel time but not trip frequency highlighting the complex effects of caregiving roles. Car access remains the strongest determinant of mode choice, with lower-income car owners showing higher car dependency than higher-income ones. Public transport subscriptions—especially monthly passes—strongly increase weekday transit use. On weekends, older adults and women show greater openness to sustainable modes like walking and cycling, indicating untapped potential for non-car mobility.