Background: This article identifies and examines key performance indicators (KPIs) related to citizen mobility logistics in smart and sustainable urban areas. It begins with a comprehensive literature review to identify essential KPIs, offering valuable insights for both public and private stakeholders, including policymakers and mobility service providers. Drawing from various mobility projects in smart cities, the study extracts common KPIs and best practices. The focus of the paper then turns to Barcelona, Spain, where KPIs that matter most are analyzed. Methods: Using open data from the city council spanning from 2017 onwards, the study provides insights into the evolving mobility logistics landscape. KPIs from other European cities are also considered by utilizing similar open data sources. This comparative analysis provides valuable benchmarks and reveals disparities in mobility logistics. Throughout this investigation, the paper emphasizes the role of data quality in KPI selection. Results: Reliable open data significantly influence indicator choices and present challenges when comparing cities. Remarkably, the findings consistently highlight environmental data as an area requiring attention in sustainable mobility logistics. Conclusions: This paper makes contributions by identifying and examining KPIs relevant to citizen mobility logistics in smart and sustainable urban areas. It offers insights by applying these KPIs to Barcelona and conducting comparative analyses with other European cities. These findings serve as a valuable resource for policymakers, city planners, and mobility experts.

In the financial sector, insurance companies generate large volumes of data, including policy transactions, customer interactions, and risk assessments. These historical data on established customers provide opportunities to enhance decision-making processes and offer more customized services. However, data on potential new customers are often limited, due to a lack of historical records and to legal constraints on personal data collection. Despite these limitations, accurately predicting whether a potential new customer will generate benefits (high-performance) or incur losses (low-performance) is crucial for many service companies. This study used a real-world dataset of existing car insurance customers and introduced advanced machine learning models, to predict the performance of potential new customers for whom available data are limited. We developed and evaluated approaches based on traditional binary classification models and on more advanced boosting classification models. Our computational experiments show that accurately predicting the performance of potential new customers can significantly reduce operation costs and improve the customization of services for insurance companies.

The growing problem of nitrogen dioxide (NO₂) pollution in urban environments is driving cities to adopt smart and sustainable approaches to address this challenge. To quantify and compare the effect of environmental policies, cities must be able to make informed decisions with real-time data that reflect the actual situation. Therefore, the objective of this work is threefold: The first is to study the behavior of the key performance indicator (KPI) of NO₂ concentrations per station in Barcelona through exploratory analysis and clustering. The second is to predict NO₂ concentration behavior, considering meteorological data. Lastly, a new distribution of current and new stations will be proposed using an optimization algorithm that maximizes the distance between them and covers the largest area of the city. As a result of this study, the importance of the location of measurement points and the need for better distribution in the city are highlighted. These new spatial distributions predict an 8% increase in NO₂ concentrations. In conclusion, this study is a comprehensive tool for obtaining an accurate representation of NO₂ concentrations in the city, contributing to informed decision-making, helping to improve air quality, and promoting a more sustainable urban environment.

Evaluating urban mobility transitions is essential to determine whether local transport interventions support broader sustainability goals. Cities increasingly implement initiatives to promote public transport, active mobility, and low-carbon transport systems. Still, assessing their impact on city-scale structural change remains challenging. Existing evaluation approaches often rely on project-level monitoring or fragmented indicators, which limits cross-city comparison and the assessment of long-term system transformation. This paper proposes a dual-track methodology to evaluate sustainable urban mobility interventions. The first track uses city-defined key performance indicators to capture local implementation processes, governance dynamics, and perceived outcomes. The second track relies on publicly available open data to assess city-scale changes in mobility indicators, including public transport accessibility, cycling infrastructure provision, and traffic-related air pollution. The methodology is applied to ten European cities using open data and satellite-based environmental indicators. Results indicate that while cities report progress at the project level, external indicators show limited short-term structural change in city-wide mobility systems. These findings highlight the value of open data as an independent evaluation layer that contextualises local results and supports transparent assessment of urban mobility transitions.

In insurance portfolios, classifying customers without a prior history at a given company is particularly challenging due to the absence of historical behavior, extreme class imbalance, heavy-tailed loss distributions, and strict operational constraints. Traditional machine learning approaches, including the baseline methodology proposed in previous studies, typically optimize global predictive accuracy and therefore fail to capture business-critical outcomes, especially the identification of high-risk clients. This study extends the existing approach by evaluating two complementary business-aware classification strategies: (i) a balanced bagging ensemble specifically designed to handle class imbalance and maximize expected profit under explicit customer-omission constraints, and (ii) a lightweight Transformer-based architecture capable of learning richer feature representations. Both approaches incorporate the asymmetric financial cost structure of insurance and operate under operational selection limits. The empirical analysis is conducted on a proprietary large-scale auto insurance dataset comprising 51,618 customers and is complemented by validation on nine synthetic datasets to assess robustness. Model performance is evaluated using statistical tests (ANOVA, Friedman, and pair-wise comparisons) together with business-oriented metrics. The results show that both proposed approaches consistently outperform the baseline methodology (p < 0.001) in terms of profit, with the ensemble offering a better balance of performance and efficiency, while the Transformer shows stronger robustness and generalization under data perturbations. The balanced ensemble provides the most favourable trade-off between predictive performance, robustness, interpretability, and computational efficiency, making it suitable for deployment in regulated insurance environments, while the Transformer achieves competitive results and exhibits stronger generalization under data perturbations. The proposed approach aligns machine learning with actuarial portfolio optimization by explicitly integrating profit-driven objectives and operational constraints, offering two practical and scalable solutions for risk-based decision-making in real-world insurance settings.