Periodic Reporting for period 1 - ReMuNet (Resilient Multimodal freight Transport Network)
Reporting period: 2023-07-01 to 2024-12-31
ReMuNet addresses vulnerabilities in Europe’s freight transport network by creating a digitally integrated, multimodal system that is both realistic and standardized. The project develops a unified framework—including a common ontology and robust data management protocols—to improve coordination among diverse stakeholders such as freight forwarders, terminal operators, SMEs, and civil protection organizations.
Key project objectives include: • Establishing comprehensive standards for a uniform modelling language (KO#1). • Reducing inland transport emissions by promoting lower-emission routes and evaluating alternative drive technologies (KO#2). • Enhancing network responsiveness to disruptions through advanced, real-time routing algorithms (KO#3 and KO#4). • Expanding participation in the multimodal ecosystem, especially by integrating SMEs (KO#5). • Improving predictive accuracy of disruptive impacts using AI-supported models (KO#6). • Supporting civil protection logistics (KO#7). • Creating a unified, secure real-time data pool to support decision-making (KO#8).
Early developments—such as a multimodal transport ontology, dynamic routing algorithms, and an integrated data pool—provide the technical foundation for increasing network resilience and efficiency. Practical use cases, including optimized synchromodal relay transport and rapid crisis response pilots on key corridors like North Sea-Baltic and Rhine-Danube, demonstrate the project's societal, economic, and environmental benefits.
By combining data-driven decision-making, stakeholder engagement, and innovative technology, ReMuNet lays the groundwork for regulatory alignment, policy support, and future investments in sustainable and resilient European freight transport.
Key project objectives include: • Establishing comprehensive standards for a uniform modelling language (KO#1). • Reducing inland transport emissions by promoting lower-emission routes and evaluating alternative drive technologies (KO#2). • Enhancing network responsiveness to disruptions through advanced, real-time routing algorithms (KO#3 and KO#4). • Expanding participation in the multimodal ecosystem, especially by integrating SMEs (KO#5). • Improving predictive accuracy of disruptive impacts using AI-supported models (KO#6). • Supporting civil protection logistics (KO#7). • Creating a unified, secure real-time data pool to support decision-making (KO#8).
Early developments—such as a multimodal transport ontology, dynamic routing algorithms, and an integrated data pool—provide the technical foundation for increasing network resilience and efficiency. Practical use cases, including optimized synchromodal relay transport and rapid crisis response pilots on key corridors like North Sea-Baltic and Rhine-Danube, demonstrate the project's societal, economic, and environmental benefits.
By combining data-driven decision-making, stakeholder engagement, and innovative technology, ReMuNet lays the groundwork for regulatory alignment, policy support, and future investments in sustainable and resilient European freight transport.
In WP1 the project established a clear basis for understanding the European multimodal transport ecosystem. Tasks 1.1,provided a detailed analysis of stakeholder needs, while T 1.3 1.4 1.5 developed a typology of disruptive events, and carried out root-cause and impact assessments along key TEN-T corridors. These efforts offer a realistic picture of the current operational challenges and requirements for improved data integration and risk management. Together with T 1.2 describing the standardization in multimodal transport ecosystem.
WP2 built on these insights by working on technical solutions. Through Tasks 2.1 to 2.4 the project developed adaptive routing algorithms and integrated intermodal data into a test system. This system, which incorporates digital networks, terminals, timetables, and operator information via server-based APIs, demonstrates a practical approach to dynamic, synchromodal route planning under disruptive conditions.
WP3 focused on establishing a robust data management framework and mapping the multimodal transport ecosystem. Tasks 3.1 was used to create a Data Management Plan , ensuring that data handling aligns with FAIR principles and GDPR requirements. T3.2 focused on the entire ecosystem description with the financial, information and goods flow.
Collectively, the work packages provide a structured pathway from a comprehensive analysis in WP1, through technical development in WP2, to data management and ecosystem integration in WP3. The achievements so far offer a realistic foundation for developing standardized frameworks and digital solutions to enhance the resilience and efficiency of European multimodal transport corridors. While challenges remain, the project has set a clear roadmap for addressing operational issues and adapting to disruptive events in the transport network.
WP2 built on these insights by working on technical solutions. Through Tasks 2.1 to 2.4 the project developed adaptive routing algorithms and integrated intermodal data into a test system. This system, which incorporates digital networks, terminals, timetables, and operator information via server-based APIs, demonstrates a practical approach to dynamic, synchromodal route planning under disruptive conditions.
WP3 focused on establishing a robust data management framework and mapping the multimodal transport ecosystem. Tasks 3.1 was used to create a Data Management Plan , ensuring that data handling aligns with FAIR principles and GDPR requirements. T3.2 focused on the entire ecosystem description with the financial, information and goods flow.
Collectively, the work packages provide a structured pathway from a comprehensive analysis in WP1, through technical development in WP2, to data management and ecosystem integration in WP3. The achievements so far offer a realistic foundation for developing standardized frameworks and digital solutions to enhance the resilience and efficiency of European multimodal transport corridors. While challenges remain, the project has set a clear roadmap for addressing operational issues and adapting to disruptive events in the transport network.
Reference logistics models (KO #1) [TRL 5]:
ReMuNet will complement existing logistics models by including a holistic view of multiple transport modes. Additional variables will be integrated, and several issues regarding the understanding and definition will be clarified. Throughout the project, this approach will manage increasing complexity that arises from adding further modes of transport and will establish a “common language” for the scientific and economic community.
Route planning algorithms (KO #3, #4) [TRL 7]:
ReMuNet builds on state-of-the-art multimodal routing techniques and incorporates disruption variables. Several algorithms will be compared based on their performance under disruptive conditions. The resulting algorithm will support the ReMuNet collaborative platform in providing dynamic, real-time route alternatives to logistics operators in the event of disruptions.
Multimodal transport platforms (KO #5, #7) [TRL 7]:
ReMuNet surpasses current practice by combining a sharing economy approach with a comprehensive transport solution in a single system. This design increases resilience through risk diversification, while lowering emissions by shifting more freight off the roads. A broad set of stakeholders—ranging from small local carriers to large-scale integrators—will be integrated into one data-driven ecosystem.
Disruptive event modelling (KO #6) [TRL 4]:
Beyond developing a robust routing algorithm, ReMuNet applies reinforcement learning to predict the impact of disruptive events. This innovation organizes sustainable route alternatives to bypass disruption-induced bottlenecks and continuously adapts to scenario types and events. By incorporating region- and route-specific risk analyses, ReMuNet closes the gap from conceptual modelling to validated industry-scale pilots, enabling more reliable, data-driven decision-making.
ReMuNet will complement existing logistics models by including a holistic view of multiple transport modes. Additional variables will be integrated, and several issues regarding the understanding and definition will be clarified. Throughout the project, this approach will manage increasing complexity that arises from adding further modes of transport and will establish a “common language” for the scientific and economic community.
Route planning algorithms (KO #3, #4) [TRL 7]:
ReMuNet builds on state-of-the-art multimodal routing techniques and incorporates disruption variables. Several algorithms will be compared based on their performance under disruptive conditions. The resulting algorithm will support the ReMuNet collaborative platform in providing dynamic, real-time route alternatives to logistics operators in the event of disruptions.
Multimodal transport platforms (KO #5, #7) [TRL 7]:
ReMuNet surpasses current practice by combining a sharing economy approach with a comprehensive transport solution in a single system. This design increases resilience through risk diversification, while lowering emissions by shifting more freight off the roads. A broad set of stakeholders—ranging from small local carriers to large-scale integrators—will be integrated into one data-driven ecosystem.
Disruptive event modelling (KO #6) [TRL 4]:
Beyond developing a robust routing algorithm, ReMuNet applies reinforcement learning to predict the impact of disruptive events. This innovation organizes sustainable route alternatives to bypass disruption-induced bottlenecks and continuously adapts to scenario types and events. By incorporating region- and route-specific risk analyses, ReMuNet closes the gap from conceptual modelling to validated industry-scale pilots, enabling more reliable, data-driven decision-making.