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From Scholars for Scholars

Emotions are relatively brief, object-directed affective episodes that arise when a person appraises an event, place, object, or memory as relevant to their concerns, and that unfold across several coordinated components: cognitive appraisal, physiological change, motivational and action tendencies, motor expression, and subjective feeling. Emotion is distinguished from the broader, longer-lasting and often objectless state of mood, from core affect (the elementary, always-present feeling of pleasantness and activation), and from feeling (the conscious, subjective component of an emotional episode). In relation to cultural heritage, “emotions and emotion-related values” denote (i) the affective responses that people have to heritage places, objects, and practices, and (ii) the dimensions of heritage significance—variously termed social, communal, spiritual, and associative value—that are constituted through, and cannot be assessed independently of, those affective bonds. On this view, heritage is not an inert set of material remains whose value is intrinsic and expert-determined; it is an active, meaning-making and emotionally charged cultural process through which individuals and communities relate to the past in the present.

Artificial intelligence (AI) and machine learning (ML) are increasingly applied to road traffic congestion prediction, but heterogeneous outcomes, models, horizons, and evaluation practices limit comparability. The objective of this study was to synthesize methods, applications, validation, explainability, and reproducibility in AI/ML-based road traffic congestion prediction and forecasting. Following PRISMA 2020, Scopus, Web of Science Core Collection, and IEEE Xplore were searched through 5 July 2026 for English-language journal articles and full conference papers published from 2000 to 2026. Two external reviewers independently screened 734 unique records and assessed the retrieved full texts, while the author resolved disagreements against the predefined eligibility criteria. Study characteristics, prediction tasks, congestion indicators, model families, metrics, explainability, validation, and data/code availability were synthesized descriptively and narratively. Of 1131 records identified, 397 duplicates were removed and 734 records were screened. Full-text retrieval was sought for 339 reports; 195 could not be retrieved, 144 were assessed for eligibility, and 129 were included. Congestion level was the main prediction task, while traffic flow and speed were the most frequent indicators. Heterogeneity and the absence of verified numerical performance values precluded meta-analysis or model ranking. Explainability was limited, and external validation, transferability, and reproducibility were insufficiently documented. Progress requires standardized outcomes, transparent validation, reproducible workflows, explainable models, and independent testing across networks and cities.