The climate objectives necessitate a modal shift of freight transport from road to rail, with single wagon load (SWL) transport playing a critical role due to its direct competition with truck transport. This paper investigates the impact of innovative freight wagon concepts on enhancing the competitiveness of SWL transport. Interviews with professionals from the SWL sector were conducted using a qualitative single-case study approach. The findings reveal that retrofitting conventional freight wagons with innovative designs can significantly influence operational processes, including wagon disposition planning, turnaround times, and other key performance metrics. These improvements are likely to boost the competitiveness of SWL transport, contributing to an increased share of rail in the modal split. This study adds to the literature on SWL transport by offering a focused analysis of how innovative freight solutions can address the sector's operational challenges. Furthermore, it provides practical insights for managers, emphasising the importance of adopting innovative wagon technologies to optimise efficiency, reduce costs, and strengthen the market position of SWL transport in the evolving freight sector.

Rail pods are an emerging concept of modular self-propelled rail vehicles which can interchangeably move freight and passengers to provide a more customer-oriented rail service. Pods are envisaged to operate on demand with the possibility of forming platoons either physically (e.g. mechanical or digital couplers) or virtually (e.g. by radio communication) coupling at stations. This study addresses the need to understand how rail pod platoons affect rail corridor capacity by analysing the actual infrastructure occupation under different platoon compositions, taking into account train movement dynamics and signalling constraints. First, this study extends the consolidated rail capacity assessment method (UIC Code 406) by applying the blocking time theory to assess the infrastructure occupation of the rail pod platoons. Based on this extension, a nonlinear optimization model is developed to determine coordinated speed profiles that are structurally consistent with the platoon configuration, aiming to minimize rail capacity utilization. The model is applied to a case study considering the ETCS Level 2 signalling system. The results obtained for the case study illustrate the ability of the proposed model to identify operational speeds and composition of rail pod’ platoons that lead to the effective capacity use of the existing infrastructure. This capacity assessment framework provides a theoretical foundation for flexible allocation of modular rail cars in dynamically structured platoons.

Disruptions and uncertainties can significantly reduce the efficiency of conventional intermodal transport, often leading to severe economic losses and deterioration in service levels. To mitigate the negative impacts of disruptions on the shipments, our research leverages the flexibility of synchromodality and develops a learning-based modular framework for disruption management. By utilizing a hybrid simulation-optimization modeling approach, the framework effectively captures disruptions and generates dynamic response strategies. Through the integration of Reinforcement Learning (RL), the proposed approach re-plans under disruptions, accounting for their stochastic characteristics, enabling swift and effective decision-making in real-time scenarios. Results are compared against two policies, always wait and always reassign, highlighting the superior performance of the RL approach, when exposed to a certain disruption profile, with comparable or better decisions compared to other policies in response to disruptions. Additionally, results are compared against a benchmark policy to test an alternative reward mechanism, demonstrating that integrating a cost-based reward mechanism increases its resilience and results in lower costs, especially in the case of more frequent and low to moderately severe disruptions.

Modular vehicles (MVs), equipped with autonomous driving, communication, and platooning capabilities, are emerging as a promising innovation in transportation, offering the potential to enhance operational efficiency, flexibility, and environmental sustainability. However, challenges and barriers to their successful implementation are not yet fully understood, which limits the realization of these benefits. This literature review synthesizes existing research on MVs across various applications, including passenger and freight transport, to provide a systematic evaluation of state-of-art, opportunities and challenges for modular freight transport systems. The review identifies research gaps in five areas, such as their integration with multimodal transportation, and highlights key deployment challenges including regulatory hurdles, human factors, financial constraints, and operational complexities. Our findings emphasize the need for policy development, system design research and further empirical validation to assess the practical feasibility and impacts of MVs in the freight transport sector.

The Physical Internet (PI) paradigm, which has gained attention in research and academia in recent years, leverages advanced logistics and interconnected networks to revolutionise the way goods are transported and delivered, thereby enhancing efficiency, reducing costs and delays, and minimising environmental impact. Within this system, PI-hubs function similarly to cross-docks, enabling the splitting of PI-containers into smaller modules for delivery through a network of interconnected hubs. This allows dynamic routing optimisation and efficient consolidation of PI-containers. However, the impact of system parameters and relevant uncertainties on the performance of this innovative logistics framework is still unclear. For this reason, this work proposes a robustness analysis to understand how the PI logistics framework is affected by the handling, consolidation, and processing of PI-containers at PI-hubs. To this end, the considered PI logistics system is represented via a mathematical programming model that determines the best allocation of PI-containers in an intermodal setting with different transportation modes. In doing so, four Key Performance Indicators (KPIs) are separately considered to investigate different aspects of the PI system's performance, and the relevant robustness is assessed with respect to the PI-hub processing times and the number of modules per PI-container. In particular, a Global Sensitivity Analysis (GSA) is performed to evaluate, through a case study, the individual relevance of each input parameter on the resulting performance.

Large-scale electrification of heavy-duty road freight faces challenges including scarcity of charging infrastructure and high battery costs. Dynamic charging could help overcome these challenges by enabling trucks to charge while driving. Important additional benefits for carriers related to lower required sizes and longer lifetimes of batteries could justify the required investments. The study investigates the optimal configuration of network sections to be electrified so that the balance between costs and benefits turns out positive. A case study for a highway network spanning 4 countries in Europe suggests that dynamic charging can lead to a significant reduction in overall transport system costs, up to very large network sizes. The study supports the decision-making of policymakers and road authorities by providing new insights into the costs and benefits of dynamic charging networks, and simultaneously considering the perspectives of investors and users.

Under the pressing need for increasing sustainability of freight transport, the rail sector faces challenges in competing with its road counterpart which can be contributed to lower flexibility and reliability. To overcome these challenges, intermodal rail freight systems should undergo a transformative evolution to enhance flexibility and seamlessly integrate with broader mobility services. Crucial to the success of any such innovative solution is integration with the existing railway infrastructure. This study explores the concept of modular vehicles (MV s) in rail systems, focusing particularly on the innovative 'Pod' system introduced in the Pods4Rail project. It introduces a framework for Pods scheduling on the railway network, incorporating an overlap-level-based platooning. Experiments show that implementing this system can significantly reduce the makespan and optimize railway capacity utilization, especially when dealing with larger problem sizes.

Climate change stresses the need for research and development of innovative sustainable mobility solutions that provide reliable and convenient door-to-door services for both passengers and freight. The increase in urban population and the popularity of e-commerce further highlights the need for action. In this regard, crowd-shipping is often perceived as an efficient, cost-effective, and sustainable alternative (or complement) to the management of urban freight mobility through efficient utilization of current transportation capacities. In this framework, inspired by the concept of MaaS (Mobility as a Service) in integrating various forms of transport and transport-related services into a single on-demand mobility service, this paper proposes a car-sharing-based service for the combined mobility of passengers and freight. In doing so, one-way car-sharing and crowd-shipping concepts are integrated in order to serve part of the existing freight demand in a sustainable and cost-efficient way for users, societies, and the environment. An optimization model is proposed to optimally plan the activation of one-way car-sharing and crowd-shipping services and to determine the optimal number of vehicles to assign to them. Such decisions are aimed at minimizing the total imbalance by serving passenger and freight demand during different time periods. In doing so, the willingness of users to carry freight in their vehicles is also taken into consideration. The capability of the proposed approach is evaluated through representative numerical examples aimed at showing the impact of the model parameters on the solution.

The increasing volume of global freight trade, coupled with economic growth, necessitates ongoing innovation in optimizing freight operations. Over the past decade, the concept of synchromodality has been explored to encourage a modal shift from unimodal to multimodal transport. Synchromodality, with its flexibility feature, can create more resilient freight transport systems. Various models employing different techniques have been proposed to establish a resilient synchromodal framework capable of reacting to disruptions. However, there are only few studies addressing the unknown duration of disruptions. This research proposes a learning-based modular framework comprising to capture the dynamics of disruptions in multimodal transport and learn to make more effective decisions, thus addressing the challenge of limited prior knowledge about disruptions and enabling fast responses to disruptions.

This paper explores the enhancement of rail-based, intermodal freight transport systems through the integration of modular vehicles (MVs), aiming to boost the flexibility of this form of transport and its integration into the mobility-as-a-service framework. This study specifically addresses a specific variant of the Pickup and Delivery Problem (PDP) tailored to railway logistics, by developing mathematical models for the Pickup and Delivery Modular Vehicle Routing Problem (PDMVRP) with platooning. This model focuses on the operational challenges associated with routing and platooning of MVs on the railways. A case study conducted within a railway context assesses the efficacy of our model, demonstrating its potential to reduce transportation costs and improve railway capacity utilization, thereby advancing modular vehicle operations in railway environments.