Maritime transport in the European Union has increased in the last years, triggering congestion in many of the most important sea and river ports. A lot of works have highlighted how the connection between these ports and dry ports can contribute to reducing port congestion and emission of greenhouse gases (GHGs). This work aims to improve the knowledge about the functionalities of Terminal Operating Systems (TOSs) managing container terminals of sea, river, and dry ports, with the aim of improving their performance and contributing to reducing congestion and GHG emissions to achieve a higher sustainability. The contribution and novelty of this paper in the field of container-terminals logistics research is the use of the Analytic Hierarchy Process (AHP) to identify and hierarchize TOS functionalities. The robustness of the model was checked by applying a sensitivity analysis. One hundred and seven functionalities were grouped into six main clusters: Warehouse, Maritime Operations, Gate, Master Data, Communications, and ERP (Enterprise Resource Planning) Dashboard. The results show that time tracking of vessels, space optimization, development of loading and unloading lists, and optimization of container locations are the most important functionalities of a TOS. This work is addressed to developers, sellers, managers, and users of TOSs and researchers working on container-terminal performance.

The increased number of transports with dangerous goods (TDG) increases the risk of both accidents and terrorist attacks. Digitalisation is crucial in order to avoid human errors and to increase safety, security and efficiency, however there are few research studies that analyse digitalisation for the TDG, this being the first time, to the author’s knowledge, that barriers to the implementation of information and communication technology (ICT) solutions for the transport of dangerous goods has been analysed. A new elicitation methodology called the ‘implementation acceleration methodology for ICT (IAM-ICT)’ has been created, with the aim of identifying and hierarchizing potential barriers to the implementation and integration of ICT solutions involved in the multimodal TDG, and with a view to overcoming the most relevant problems at an early stage. This methodology combines user-driven innovation (UDI), the analytic hierarchy process (AHP) and Bayesian networks. The application of this methodology, which is easily replicable in other fields, will enable a better design of ICT solutions so that they can be implemented and integrated in SMEs and large companies. The results show a hierarchy of potential barriers for a multi-modular ICT solution used as use case study, in two different co-designed schemes for a small and medium enterprise (SME) and for a large company.

This work analyzes gendered processes by a methodology based on clustering factors with influence in the decision-making process of women as users or employees of the transport system. Considering gender as a social construction which changes over time and space, this study is based on the concept of a woman as a person who adopts this role in society. This paper performs a deep analysis of those factors women consider as needs and barriers to use or work in the transport system in four scenarios: railway public transport infrastructures, automated vehicles, bicycle sharing, and jobholders. A literature review and focus group discussions were performed under the consideration that the definition of woman includes the addition of several personal characteristics (age, sexual orientation, family responsibilities, and culture). The data analysis allowed the identification of fairness characteristics (FCs) that affect the interaction of women with the transport system for each scenario. A methodology for clustering the fairness characteristics identified the main areas of action to improve the inclusion of women within each use case. Further studies will be focused on the quantification and prioritization of the FCs through mathematical methods and the suggestion of inclusive measures by an interdisciplinary panel.