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Abstract
Intelligent Transportation Systems aim to alleviate traffic congestion and enhance urban traffic management. Transformer-based methods have shown promise in traffic prediction due to their capability to handle long-range dependencies. However, they disregard local context during parallel processing and can be computationally expensive for large traffic networks. On the other hand, they miss the hierarchical information hidden in regions of large traffic networks. To address these issues, we introduce CSCN, a novel framework that clusters traffic sensors based on data similarity, employs clustered multi-head self-attention for efficient hierarchical pattern learning, and utilizes causal convolutional attention for capturing local temporal trends. In addition to these advancements, we integrate snapshot ensemble learning into CSCN, allowing for the exploitation of diverse snapshots obtained during training to enrich predictive performance. Evaluations of real-world data highlight CSCN's superiority in traffic flow prediction, showcasing its potential for enhancing transportation systems with improved accuracy and efficiency.

Abstract
For seven years, the Text2Story Workshop series has fostered a vibrant community dedicated to understanding narrative structure in text, resulting in significant contributions to the field and developing a shared understanding of the challenges in this domain. While traditional methods have yielded valuable insights, the advent of Transformers and LLMs have ignited a new wave of interest in narrative understanding. The previous iteration of the workshop also witnessed a surge in LLM-based approaches, demonstrating the community’s growing recognition of their potential. In this eighth edition we propose to go deeper into the role of LLMs in narrative understanding. While LLMs have revolutionized the field of NLP and are the go-to tools for any NLP task, the ability to capture, represent and analyze contextual nuances in longer texts is still an elusive goal, let alone the understanding of consistent fine-grained narrative structures in text. Consequently, this iteration of the workshop will explore the issues involved in using LLMs to unravel narrative structures, while also examining the characteristics of narratives generated by LLMs. By fostering dialogue on these emerging areas, we aim to continue the workshop's tradition of driving innovation in narrative understanding research. Text2Story encompasses sessions covering full research papers, work-in-progress, demos, resources, position and dissemination papers, along with one keynote talk. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Purpose Citizen engagement plays a crucial role in transitioning to sustainable service ecosystems. While customer engagement has been extensively studied in service research, citizen engagement has received significantly less attention. By synthesizing customer and citizen engagement literatures, this study develops an integrated framework to conceptually clarify the dual role of customer-citizen engagement for sustainability. Design/methodology/approach This study builds on a systematic literature review of customer engagement literature in service research and citizen engagement literature. Following a theory synthesis approach, we qualitatively analyzed 126 articles to develop an integrated conceptual framework of customer-citizen engagement for sustainability through a process of abductive reasoning. Findings The analysis showed that customer engagement and citizen engagement literatures have developed mostly separately but provide complementary views. While the customer engagement literature has traditionally focused on business-related facets, such as engagement with brands, the citizen perspective broadens the engagement scope to other citizens, communities and society in general. The integrated framework highlights the interplay between citizen and customer roles and the impact of their relationships with multiple objects on sustainability. Originality/value This integrated framework contributes to advancing our understanding of customer-citizen engagement, broadening the scope of subject-object engagement by examining the interplay between these roles in how they engage for sustainability and moving beyond the traditional dyadic perspective to a multi-level perspective of service ecosystems. This framework also enables the development of a set of research directions to advance the understanding of engagement in sustainable service ecosystems.

Abstract
We propose an approach to cluster and classify compositional data. We transform the compositional data into directional data using the square root transformation. To cluster the compositional data, we apply the identification of a mixture of Watson distributions on the hypersphere and to classify the compositional data into predefined groups, we apply Bayes rules based on the Watson distribution to the directional data. We then compare our clustering results with those obtained in hierarchical clustering and in the K-means clustering using the log-ratio transformations of the data and compare our classification results with those obtained in linear discriminant analysis using log-ratio transformations of the data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.