Abstract
Achieving sustainable urban mobility requires shifting travelers toward public transport. However, policies often assume uniform preferences, leaving a critical gap in understanding how different travelers prioritize mobility factors. To address this, the study examines behavioral heterogeneity among urban travelers using a data-driven clustering approach based on the relative importance assigned to cost, comfort, environmental sustainability, and flexibility. Using data from 698 respondents in the Asprela area of Porto, Portugal, a mixed-use district combining universities, hospitals, and commercial facilities, the study applies principal component analysis (PCA) and K-means clustering to derive distinct traveler profiles. Unlike segmentation based solely on socio-demographics or observed mode choice, this approach groups individuals according to their underlying value structures. Six clusters were identified, ranging from car-dependent, comfort-oriented users to environmentally conscious and low-engagement groups. The findings show that one-size-fits-all policies are unlikely to address behavioral diversity effectively. Building on these insights, the study proposes tailored and cross-cutting policies to enhance the attractiveness of public transport and promote sustainability. By uncovering latent preference structures, the study contributes to more inclusive and value-informed mobility planning.

Abstract
Quality performance in manufacturing has a direct influence on efficiency, generated waste, and costs. In collaboration with a textile manufacturer as a case study, this paper develops an automated defect detection system for a weaving process and evaluates its impact on operational performance. The system identifies defects immediately at their onset and prevents their propagation to subsequent fabric and production stages. A deep learning image classification model is developed, with six well-established network architectures being compared, leveraging a non-invasive image acquisition method that averts machinery disturbances for data collection. Based on the best-performing model, key indicators of operational performance are estimated using Markov Chain modelling, addressing a gap in linking model performance to operational impacts. Notable operational gains are demonstrated, namely a cost reduction of 1.3% and over 90% of waste reduction. A sensitivity analysis guides the definition of the image acquisition frame rate to minimise false alarms and shows that different operational indicators are impacted differently by different predictive performance metrics, affecting model selection. This research not only underscores the potential of integrating deep learning into textile production but also guarantees the effective communication of its impact to industry stakeholders, thus offering valuable practical insights to enhance operational performance.