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.

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.

The railway scheduling problem concerns the determination of trains' scheduled departure and arrival times at stops, and the allocation of capacity in the network. The timetable must be both conflict-free given infrastructure constraints, and stable enough for trains to recover from delays that could occur in normal operations. Existing methods for tactical scheduling contain a tradeoff between having an accurate (microscopic) representation of signalling constraints, and having a simple-enough (macroscopic) infrastructure representation to scale to real-world problem instances. This creates issues for infrastructure managers looking to run more trains on their infrastructure by migrating to Distance-To-Go (DTG) signalling systems (e.g. ETCS Level 2), and to exploit the capabilities of Connected Driver Advisory Systems (C-DAS) and Automatic Train Operation (ATO) to control trains more precisely. In this paper, we present a methodology for incorporating the capabilities of DTG signalling in conjunction with C-DAS and ATO systems into a disjunctive scheduling model for both periodic and nonperiodic instances. We show that the resulting model has both a microscopic infrastructure representation, and a macroscopic computational complexity, allowing railways to quickly compute conflict-free and stable timetables for large problem instances. The resulting model also accurately represents the computation of the brake indication point for both conventional and DTG signalling as a function of the trains' current speed. Tests on a large-scale periodic scheduling instance in the UK show that the model produces timetables with reasonable computation time.

A multimodal pod system features modular autonomous vehicles consisting of detachable transport units mounted on mode-specific carriers. Within this system, transport units arriving at the railway network need to be assigned to available rail carriers to continue their journey. For better alignment between carrier availability and transport demand, rail carriers are allowed to relocate empty within the network. This work presents a mathematical optimisation model for the associated rail carrier assignment and relocation problem. The model's performance is demonstrated using a small case study, which highlights the impact of the number of rail carriers and their initial distribution in the railway network on the number of handled transport units and total relocation time. Specifically, increasing the number of rail carriers from two to three resulted in an average of 1.25 additional transport units to be assigned, with an average 55% increase in relocation time. Furthermore, varying the initial distributions of rail carriers lead to difference of up to transport units assigned,
while relocation times varied by as much as 120% of the average relocation time. To enable a more general assessment, future work will apply the model to a larger and more realistic case study.

This paper investigates the potential improvements in multimodal freight systems through the use of modular vehicles (MVs), aiming to enhance operational adaptability and integrate these vehicles within the mobility-as-a-service (MaaS) concept. The research examines a specialized form of the Pickup and Delivery Problem (PDP) adapted for rail and road logistics, presenting a new mathematical model for the Pickup and Delivery Modular Vehicle Routing Problem (PDMVRP) that includes road and rail. This model tackles significant operational issues such as routing of MVs on road, platooning and the scheduling of MVs on railways and transfers between road and rail MVs. A practical example in a regional road and railway network demonstrates the effectiveness of the proposed model, indicating their ability to decrease costs(energy and emissions), shorten travel times, and boost the efficiency of rail capacity, thereby promoting the advancement of MV usage in future transport systems.

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.

Rail Pods are an emerging concept of modular self-propelled rail vehicle which can interchangeably move freight and transport for a more customer-oriented rail service. Pods are envisaged to operate on-demand with the possibility of forming platoons by either physically or virtually coupling at stations. In such a context, it is essential to quantify the actual service capacity of rail pod platooning, taking into account heterogeneous convoy structures and infrastructure constraints such as signalling rules and block occupation. This study extends UIC Code 406/blocking time theory to applied to Pod platoons, proposing a novel optimization model that integrates traction, cruising, and braking speed profiles alongside safe separation constraints. This approach enables coordinated optimization of cruising speeds across various platoon structures to minimize track capacity consumption. The model is applied to a case study considering the ETCS Level 2 signalling system. The results obtained for such a case study illustrate the ability of the proposed model to identify operational speed and composition of rail pods’ platoons which lead to capacity effective use of the existing infrastructure. The proposed method provides potentials for a more flexible allocation of modular rail cars based on demand configuration.

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.

Amid the growing need for more sustainable freight transport, the rail sector faces challenges in competing with road transport due to lower flexibility and reliability. To address these challenges, intermodal rail freight systems must evolve to enhance flexibility and integrate more seamlessly with broader mobility services. A key factor in the success of such innovations is the integration with existing railway infrastructure. This study examines the concept of modular vehicles (MVs) in rail systems, focusing on the innovative 'Pod' system developed in the Pods4Rail project. These Pods consist of detachable flat wagons (carriers) and capsules or containers (transport units) capable of moving both goods and passengers. A fundamental requirement for MVs is the assignment of carriers to transport units, allowing them to operate on the rail network. This involves , ensuring the availability of carriers at pickup points in terms of assignments of carriers to transport units as well as relocating empty carriers across the network. This may lead to empty runs and potential capacity challenges. Once assigned, complete Pod formation (combinations of carriers and transport units) form swarms or platoons through virtual coupling, which could significantly improve capacity utilization. This paper presents a heuristic framework that incorporates the assignment of carriers to transport units, and pod dispatching for pickup and delivery, including empty carrier circulation, considering parameters for the operation in virtual coupling. Results indicate that platooning reduces makespan for larger problem sizes, but requires a sufficient number of carriers.

Background: Disruptions in freight transportation—such as service delays, infrastructure failures, and labor strikes—pose significant challenges to the reliability and efficiency of intermodal networks. To address these challenges, this study introduces Adaptive Intermodal Transportation (AIT), a resilient and flexible planning framework that enhances Synchromodal Freight Transport (SFT) by integrating real-time disruption management. Methods: Building on recent advances, we propose two novel strategies: (1) Reassign with Delay Buffer, which enables dynamic rerouting of shipments within a user-defined delay tolerance, and (2) (De)Consolidation, which allows splitting or merging of shipments across services depending on available capacity. These strategies are incorporated into a re-planning module that complements a baseline optimization model and a continuous disruption-monitoring system. Numerical experiments conducted on a Great Lakes-based case study evaluate the performance of the proposed strategies against a benchmark approach. Results: Results show that under moderate and high-disruption conditions, the proposed strategies reduce delay and disruption-incurred costs while increasing the percentage of matched shipments. The Reassign with Delay Buffer strategy offers controlled flexibility, while (De)Consolidation improves resource utilization in constrained environments. Conclusions: Overall, the AIT framework demonstrates strong potential for improving operational resilience in intermodal freight systems by enabling adaptive, disruption-aware planning decisions.

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 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.

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.

Electric Road Systems offer dynamic charging infrastructure for electric trucks, by means of overhead lines or rails that conduct electricity, or through induction loops in the pavement. Charging trucks while they are moving has important advantages over stationary charging, as batteries can be smaller and drivers and cargo do not have to wait while recharging. There are significant knowledge gaps about optimal network sizes, impacts on different stakeholders and cost/benefits of these technologies. This paper summarizes 4 MSc thesis projects on these topics by students of TU Delft. Their findings support the growing insight that ERS could make the electric truck landscape more efficient than if based on stationary chargers alone.

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.

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.

This paper addresses the problem of optimizing the transport of goods in the Physical Internet (PI) framework in a multi-modal setting using a multi-objective mixed-integer linear programming (MILP) approach. The model is specifically designed to meet the requirements related to modular shipments and PI-hubs, and in particular, determines the allocation of modular shipments to each transport mode in an intermodal setting. In doing so, parallel direct connection via road, the delivery times and the transportation costs are minimized. The model is applied to a numerical case study, to test its effectiveness to enhance freight transport efficiency within the PI framework, by exploiting, in particular, all the capacities of the available vehicles. In addition, a sensitivity analysis is conducted on some model parameters, to test its reaction to changes in the supply system and in the objective priorities. Results show that all the shipments are effectively transported between the origin and the destination terminals, they are divided into modules when necessary, and the selected transport modes, allocation strategy, and delivery times vary accordingly to the objective priorities.

This special issue of the Journal of Supply Chain Management Science (JSCMS) presents four papers that were discussed at the 9th International Physical Internet Conference (IPIC) in 2023. The vision of Physical Internet (PI) has gone through a strong maturing process since its conception (Montreuil, 2011), both in terms of its practical articulation and scientific grounding. Recent reviews of the PI-literature (Treiblmaier et al., 2020; Pan et al., 2021; Chen et al., 2022; Nguyen et al., 2022; Cortés-Murcia et al., 2022) explicitly link the PI system concepts to its founding disciplines: industrial engineering (IE) and supply chain management (SCM). Together these PI innovation agendas have helped to involve IE and SCM researchers outside the PI community and have promoted the bundling of global R&D efforts into converging streams of work. In the light of these developments, JSCMS has offered to consider papers presented at IPIC 2023. The four selected and peer-reviewed contributions included in this special issue all revolve around the organization of high levels of connectivity between network services of individual carriers, leading to collaborative deployment of services in an integrated network. In the PI-jargon, this high-level connectivity is termed “hyperconnectivity”.