Abstract
This research addresses the Frequency Setting Problem (FSP) together with vehicle technology selection for bus fleet sizing and management. A decision support tool was developed that combines a multi-criteria decision analysis, using the Analytic Hierarchy Process (AHP), and an enumeration procedure. The tool assists transportation operators in selecting optimal frequencies and vehicle technologies, considering economic, social, and environmental criteria. Computational experiments performed in the city of Niterói, Brazil, demonstrate the effectiveness of the tool. Scenarios with different criteria prioritizations highlight the flexibility of the approach and emphasize the need for a balance between all the sustainability dimensions. This approach positively impacts public transportation system performance, favouring higher-capacity vehicles while considering demand, and contributing to sustainable urban mobility. © 2024 The Authors.

Abstract

Abstract
This paper examines the role of digital twins (DTs) in promoting sustainability within seaport operations and logistics. DTs have emerged as promising tools for enhancing seaport performance. Despite the recognized potential of DTs in seaports, there is a paucity of research on their practical implementation and impact on seaport sustainability. Through a systematic literature review, this study seeks to elucidate how DTs contribute to the sustainability of seaports and to identify future research and practical applications. We reviewed and categorized 68 conceptual and practical digital applications into ten core areas that effectively support economic, social, and environmental objectives in seaports. Furthermore, this paper proposes five preliminary potential applications for DTs where practical implementations are currently lacking. The primary findings indicate that DTs can enhance seaport sustainability by facilitating real-time monitoring and decision-making, improving safety and security, optimizing resource utilization, enhancing collaboration and communication, and supporting the development of the seaport ecosystem. Additionally, this study addresses the challenges associated with DT implementation, including high costs, conflicting stakeholder priorities, data quality and availability, and model validation. The paper concludes with a discussion of the implications for seaport managers and policymakers.

Abstract
Traffic flow prediction is a critical component of intelligent transportation systems. This study introduces a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network for predicting traffic flow. The model utilizes traffic, weather, and holiday data. To evaluate the model's performance, three experiments were assessed: E1, using all available inputs; E2, excluding weather conditions; and E3 excluding holiday information. The model was trained using the previous 3, 12, and 24 h of data to predict traffic flow for the next 12 h, and its performance was compared with a LSTM model. Traffic predictions benefit from having a large and diverse dataset. Bi-LSTM model can capture temporal patterns more effectively than the LSTM. The MAPE value is improved in around 1% when we increase the historical from 3h to 24 h, plus 1% if Bi-LSTM model is used. Better results are obtained when contextual information is provided. These results reinforce the potential that deep learning models have in the prediction of traffic conditions and the impact of a large and varied dataset in the accuracy of these predictions.

Abstract

Abstract
Small, medium, and large organizations collect vast amounts of data with the expectation of using it to generate commercial value. Machine learning is a powerful tool for extracting valuable insights from this data and serves as a pivotal sales strategy for companies to maximize profits. This paper seeks to analyze sales data and discern patterns in sales among products that exhibit similarities, such as boxes and bags. In order to achieve this goal, was used unsupervised learning methods that allow the segmentation of groups, specifically Principal Component Analysis (PCA), k-means algorithms, and hierarchical clustering. PCA was used to identify correlated variables and find hidden patterns in the data, particularly pertaining to product families with similar sales. Elbow, Silhouette, and 30 indices methods were applied to determine the optimal number of clusters. Based on these results, it was determined the optimal number of clusters. Validation methods were employed to identify the clustering algorithm exhibiting the best performance. Stability measures evaluated the consistency of the clusters, while the cophenetic coefficient aided in determining the most effective data grouping method. After validation, the clustering algorithms were implemented. The results indicated that all clustering algorithms effectively segmented the data, with particular emphasis on the performance of the k-means algorithm. This study identified product groups with similar sales patterns and key products that impact the company’s global sales. Multivariate analysis provided a deeper understanding of sales dynamics, enabling the company to implement targeted marketing strategies and optimize resource allocation to boost bag and box sales in Portugal and other countries. © 2025 Elsevier B.V., All rights reserved.