Le Havre Port Authority is putting into service a multimodal hub terminal with massified hinterland links (trains and barges) in order to restrict the intensive use of roads, to achieve a more attractive massification share of hinterland transportation and to provide a river connection to its maritime terminals that do not currently have one. This paper focuses on the rail-rail transshipment yard of this new terminal. In the current organizational policy, this yard is divided into two equal operating areas, and in each one a crane is placed, additionally it is equipped with reach stackers to enable container moves across both operating areas. However, this policy causes poor scheduling of crane moves, because it gives rise to many crane interference situations. For the sake of minimizing the occurrence of these undesirable situations, this paper proposes a multi-agent simulation model including an improving strategy for crane scheduling. This strategy is inspired by the ant colony approach and it is governed by a new configuration for the rail yard’s working area that eliminates the use of reach stackers. The proposed simulation model is based on two planner agents, to each of which a time-horizon planning is assigned. The simulation results show that the model developed here is very successful in significantly reducing unproductive times and moves (undesirable situations) and it outperforms other existing simulation models based on the current organizational policy.

Nowadays, seaports seek to achieve a better massification (massive transportation of containers) share of their hinterland transport by promoting rail and river connections in order to more rapidly evacuate increasing container traffic shipped by sea and to avoid landside congestion. The attractiveness of a seaport to shipping enterprises depends not only on its reliability and nautical qualities but also on its massified hinterland connection capacity. Contrary to what has been observed in Europe, the massification share of Le Havre seaport has stagnated in recent years. To overcome this situation, Le Havre Port Authority is putting into service a multimodal hub terminal linked only with massified modes. In this study, we focus on rail–rail transshipment of this new terminal, specifically on minimizing unproductive situations of cranes to improve crane productivity and to speed up freight train processing. To this end, an improving agent-based engineering strategy called the “crane anti-collision strategy” is proposed and tested using multi-method simulation software (Anylogic). In a numerical study, the simulation results reveal that our developed model is very satisfactory and outperforms other existing simulation models.

Over recent years, driven by intertwined economic, social, environmental, and technological factors, urbanization has accelerated at an unprecedented pace, posing complex challenges to metropolitan transport systems. This has intensified the demand for innovative mobility solutions, notably Mobility as a Service (MaaS), which promotes a paradigm shift from private vehicle ownership to mobility consumed as a service. With rapid advances in digital technologies, MaaS has gained substantial momentum, attracting significant scholarly attention for its potential to enable intelligent and sustainable transportation systems. This study aims to provide a comprehensive conceptual foundation of MaaS and its components, and to systematically examine how artificial intelligence (AI), machine learning (ML), and big data techniques are applied in this domain. Following PRISMA guidelines, a bibliometric and systematic review was conducted on peer-reviewed articles published between 2020 and 2024 and indexed in the Scopus and Web of Science databases. The analysis classifies AI applications across four MaaS integration levels: basic, intermediate, advanced, and full integration. The results show that machine learning and basic optimization dominate at the basic level; blockchain and big data are most prominent at the advanced and full levels; and deep learning is applied across all levels, with a particularly strong presence at the advanced stage for real-time, personalized mobility solutions. The findings also indicate that while most implementations focus on developed countries, there is substantial potential for adaptation in emerging markets. The paper concludes by discussing key challenges in regulatory compliance, inclusivity, and the protection of sensitive user data, and outlines future research avenues for building socially equitable, intelligent, and sustainable MaaS ecosystems.

Container terminals are complex systems where containerized cargo undergoes a set of processing and handling operations to be delivered to their outgoing modes. A pool of decision support methods and simulation models has been developed to assist planners and managers in making decisions about daily operations. Nevertheless, most are designed for a particular terminal and not generic types. Indeed, a generic model serves as a conceptual factory to create specific models which greatly reduces the time and efforts of development; however, building such a model is no mean feat. To this aim, the paper on hand discusses the complexity of applying genericity, flexibility, and modularity in system modeling and proposes a generic architecture to build modular and flexible simulation models for container terminals. This architecture is split into a set of smaller, manageable, well-connected, and generic modules that facilitate the creation of highly parametrized specific models. An illustrative example of the architecture usage is presented in a case study, the new container terminal of Stockholm, and the resulting models were validated by subject matter experts. Finally, to prove its efficiency, a numerical study fed with real data is conducted to investigate the handling capacity of the studied system under different handling and flow scenarios. The obtained results show that the terminal handling capacity can be increased by around 50% if three to four more straddle carriers are added to the existing fleet.