The Seamless Freight cluster is part of the TRANS4M-R project and the Europe’s Rail initiative. It aims to deliver an essential contribution towards the modernization, digitalization and harmonization of multimodal rail freight. By addressing various technical enablers identified in the MAWP, Seamless Freight bridges the gaps between actors, countries, systems, processes and transport modes. Against this background, this Deliverable is the main output of the specification phase and includes the relevant requirements, both functional and non-functional, the main use-cases and use-case environments as well as a description of relevant systems, data types, processes and challenges. These specifications build on the basic requirements identified in the scope of the Deliverables D25.2 and D25.3. The contents of this deliverable are the result of an extensive and iterative process, involving relevant stakeholder groups (IMs, RUs, TOs, YOs, CTOs and shippers) that are all part of the TRANS4M-R team. The specifications are structured according to the different work packages, in which the solutions, that are necessary to fulfil the objectives of Seamless Freight, will be developed. True seamless planning must result in perfectly consistent planning and allowing for smooth transition and continuity for all actors involved along the entire transport chain as well as all assets required for operating the railway system. Seamless planning therefore encompasses all planning horizons (e.g. long- and short-term as well as real-time), all planning environments (e.g. yards, terminals and all connecting infrastructure) and involves a variety of complex planning systems and processes. All these aspects feed the derivation of requirements for planning systems and their interfaces, with additional consideration of the interconnection to dispatching and keeping the information on line and network capacity updated for all actors, in order to achieve true seamless planning. Dynamic Dispatching has focussed on the constraints of today, that hinder optimized processes due to lack of real-time information. An intensive exchange with end customers and stakeholders has led to several use cases which shall at an international level prove that harmonization and the dynamic adaption of tasks due to real-time information will lead to higher efficiency and maximizing the use of existing infrastructure. Intermodal Prediction Systems, forecasting both the ETA and the ETD for pre-defined milestones by using advanced machine-learning models, enhance the transparency and reliability of rail freight. The systems use various TAF TSI and EDIGES message types as basis input. Its quality is evaluated using pre-defined TAF TSI KPIs. Main applications for the prediction values are the optimisation of terminal and yard processes as well as the assignment and planning of rolling stock utilization (Asset Warehouse). The concept of Standardised European Railway Checkpoints is a further development of the previous work carried out in Shift2Rail and the concept of “Intelligent Video Gates” (IVG). The main objective was thus a further development of the previous work. Moreover, in FP3 Checkpoints are also developed but at main lines for both freight and passenger trains. Hence, one main aim was to give a clear and through background description, including existing similar systems that the IMs in T25.4 currently possess. Process descriptions were carried out for three types of operational stops for freight trains; intermodal terminals, marshalling yards and borders. Opportunities for improving these processes though the use of Checkpoints as well as a vast set of use cases were identified. Functional and non-requirements were developed. Based on the process analysis and the defined requirements, technical specifications were outlined for detection technologies and for data sharing. Albeit technical standardisation has been addressed, further work is needed to be carried jointly between the System Pillar sub-project Harmonized European Railway Diagnostics (Herd), FP5 T25.4/WP29 and FP3 WP7. Thus, the specifications outlined in this report will be the basis for a standardised development and installation of Checkpoints within WP29. Multimodal Integration has focussed on the constraints of today, that hinders simple bookings of freight on rail. Three primary reasons have been identified that will be tackled by use cases. The time-consuming process of finding existing freight train services, the complexity to book services if more than one primary supplying company is involved and the difficulty to establish new services where today’s offering is not yet matching the market demand. All shall demonstrate that harmonized and standardized process and data exchange will lead to higher usage of existing infrastructure due to lowering entry barriers. All this requires a high degree of collaboration between the involved actors both within and often across national borders. Today, there is a call for better synchronisation within and between transport practices. Big hopes are being placed on digitalisation as an enabler and means for integrated and sustainable performance along the multi-modal supply chain. The primary objective for enabling data exchange is to provide a framework that allows a seamless and harmonised exchange of data. This framework aims to facilitate an increased data availability and quality by reducing technical and administrative barriers for the generation and exchange of data in the project. This framework will be built on existing developments rather than introducing new elements.

Projektet har genom demonstration utvärderat nyttan av digitalisering och digital samverkan mellan järnvägssystemets aktörer för att åstadkomma en samlad lägesbild och därmed ge involverade aktörer bättre beslutsunderlag. Projektet har samlat aktörer för att samskapa och utvärdera konceptet StationCDM (Station Collaborative Decision Making). Konceptet bygger på att i realtid utbyta data om aktörers intentioner och faktiska progress i syfte att förbättra koordination och effektivitet i järnvägssystemet. Projektets fokus har varit resandetåg mellan Hagalund - Stockholm – Göteborg. Målet har varit att utvärdera nyttan av digital samverkan genom att analysera olika användningsfall, vilka ska inspirera aktörer att dela överenskommen data i realtid och på ett standardiserat sätt, och se på vilka sätt det åstadkommer högre punktlighet, bättre planeringsförmåga och förbättrad arbetsmiljö. I projektet har metoden LivingLab Approach använts för att utveckla och utvärdera, de av involverade aktörerna identifierade användningsfallen, för digital samverkan. Metod innebär att utvecklat koncept är väl förankrat hos involverade aktörer och visar fördelarna med ett digitaliserat järnvägssystem, vilket ger aktörer möjlighet att växa i digital mognad och innovationsförmåga. Ett exempel på resultat är från användningsfallet PLANKAN, där tågbildningen vid Hagalund sker i samråd mellan aktörerna Trafikverket och SJ. De planerade spåren och tiderna för ankomst samt avgång Hagalund kan genom PLANKAN visas i en gemensam lägesbild och där ändringar görs i realtid. Det har medfört en markant minskning av telefonerande och e-mailande mellan aktörerna då det i snitt görs över 150 ändringar per dygn av spår och tidpunkter. Även råd och riktlinjer för framtida implementation av StationCDM-koncept har utarbetats.

o Projektet har bidragit med en samsyn mellan aktörerna av vad som kan och behöver utbytas i form av data i syfte att åstadkomma en samlad lägesbild, uppbyggd av aktörers intentioner, omplaneringar, faktisk status för aktiviteter i och kring Malmö Gods Bangård, MGB. Genom att skapa bättre beslutsunderlag baserat på konceptet YardCDM (Yard Collaborative Decision Making), i realtid bidrar detta till förbättrad samordning och effektivitet på MGB. MGB är en komplex järnvägsnod - dels pga att det är en säckbangård, har en kombiterminal samt att MGB trafikeras av intermodala tåg till och från Danmark. Målet har varit att utvärdera nyttan av digital samverkan genom att analysera olika användningsfall genom demonstratorer, vilket ska inspirera aktörer att dela data i realtid på ett standardiserat sätt, och se på vilka sätt de bidrar till bättre samordning och effektivitet. Metoden LivingLab Approach har använts för att utveckla och testa olika koncept/användningsfall för digital samverkan. Konceptet YardCDM har utvecklats i samverkan med involverade aktörer på MGB, vilket leder till ett väl förankrat koncept som visar fördelarna med ett digitaliserat järnvägssystem, och ger aktörer möjlighet att växa i digital mognad och innovationsförmåga. Ett exempel på resultat är från användningsfallet LOKVÄXLING, där lokväxlaren digitalt via en smartphone erhåller ”sin loklista” och i den kan utföra sina arbetsuppgifter och löpande rapportera arbetsmomenten. På detta sätt blir alla som behöver realtidsinformation kring loken informerade löpande t.ex. personal på MGB men även den centrala lokledningen för de olika operatörerna t. ex. green cargo. Tidigare skrev lokväxlaren ut listan på ett papper i det så kallade ”blå huset” och kunde sedan först efter både en och två timmar vara tillbaka och mata in aktuell status på den datorn. Även råd och riktlinjer för framtida implementation av YardCDM-koncept har utarbetats.

Maritime transports are to be regarded as a self-organized ecosystem (Kay et al., 1999) characterized by sub-optimization where historically each actor to has optimized its own operations, often giving rise to inefficiencies as a whole. In recent years however, digital transformation has challenged this by providing means for enhanced transparency in data sharing and situational awareness, enabling better coordination and improved efficiency on the whole (Lind et al. 2018a). Digital transformation drives the possibilities of creating new value by enabling higher degrees of connectivity between actors, digitally twin physical objects, drawing patterns of behaviour based on extensive sets of historical data, as well as harmonizing data sharing through standardized interfaces and communication protocols (e.g. Almirall and Casadesus-Masanell 2010; Gassman et al. 2010; Lakhani et al. 2006). To break existing patterns of behaviour and to avoid the creation of proprietary solutions that feed sub-optimization, there is a need for new inspiration and perspectives that capitalize on the opportunities that digital transformation provides. From an open innovation point of view, this means that innovators both having experience from the sector as well as from other sectors would come together, come up with, and provide new applications not previously possible or never thought about before. A core capability that the ecosystem needs to develop and ensure is data streams made accessible for those that can provide new applications aimed for the single actor and/or clusters of actors, within or outside the maritime sector (Lind et al. 2018).This has also been one of the objectives for Port Collaborative Decision Making (PortCDM), which is a concept that provides guidelines and standards for the data exchange within and between ports, between ships and ports, and between ports and hinterland operators (Lind et al 2018). Such data exchange is necessary if enhanced efficiency during port call operations is to be achieved but also facilitates open 

innovation within the maritime sector. In order to realise that potential, a purposive transfer of knowledge between the established actors and potential new service providers has to be established (Chesbrough 2006). We therefore set out to explore How can open innovation intermediaries accelerate acquisition in an ecosystem through the management and throughput of knowledge transfer?We address the question through a longitudinal study by applying an action research approach involving actors from the local port and students from three bachelor programs. Before we describe the specifics of the research methodology, we outline our theoretical framework in terms of how knowledge transfer can be framed within an open innovation ecosystem. After the research methodology we detail the five iterations and then discuss the effect on knowledge transfer within the ecosystem. Finally, we conclude and give directions on future research.