Digitalization offers the transport sector great opportunity to revolutionize the way in which it does business, enabling improved interaction with customers, the use of novel business models that span transport modes, and fine-grained control of operations in real-time. Despite the potential advantages of digitalization of fleet and infrastructure, many transport networks have been slow to embrace novel technologies; accepted thinking is that this slow response in many traditional transport settings is due to the long lifecycle of the infrastructure on which many transport networks depend, coupled with the safety-led culture that pervades the sector, but is that true? Based on the outcomes of a workshop session held as part of an industrial Continuing Professional Development (CPD) program, this paper presents an industrial perspective on the challenges posed by the digitalization of the rail network of Great Britain, with a view to stimulating debate around the common digitalization challenges facing rail and other transport modes as we move forward into the 21st century.

The System for Ticketing Ubiquity with Blockchains (STUB) is a novel solution to multi-modal transport ticketing. Introduced previously using Hyperledger Fabric, STUB utilises the distributed mechanics of blockchain technology right at the core of its architecture, allowing stakeholders from different transport modes to vend and validate tickets on a shared ledger. This open approach to ticketing data will benefit transport governing bodies, transport operators, and passengers alike by ensuring cross-party cooperation and presenting a fresh holistic approach to the ticketing sector. This paper addresses issues from STUB 1.0, concerning validating tickets for a multi-modal transport system. To overcome this, we propose creating a graph structure, known as the Transport Network Graph (TNG), to represent the transport network with all of the services provided by the Transport Service Providers (TSPs). This enables the implementation of an automated Revenue Allocation System (RAS), whilst retaining the benefits provided by blockchain technology.

This paper presents a novel routing system for guiding a pedestrian within a train station, accounting for aspects such as distance, crowd density, and the pedestrian’s accessibility requirements. We achieve this by building a system that takes inspiration from the video games industry; constructing a Three-Dimensional model, annotating the model, generating a graph data structure, and performing pathfinding algorithms to establish the shortest path between two nodes. In particular, we make use of Three-Dimensional scanning technology to build a Digital Twin of Smethwick Galton Bridge station in the United Kingdom. We then use the spatial data to extract Two-Dimensional floor plans and perform Constrained Delauney Triangulation to compute a navigational mesh. The model then constructs the relevant graph data structure, on which we perform the A* Algorithm to determine the most efficient path between two coordinates within the mesh, where the efficiency is based upon both distance and the crowd density within the station.

Through advanced sensor technologies, satellite-based authentication, and high bandwidth data networks, Remote Condition Monitoring (RCM) systems are now an essential `Internet of Things' (IoT) resource for efficient operation of railway infrastructure. However, the full potential of this big data has yet to be realized. Data is currently collected and used in siloes, with limited visibility of all possible datasets for exploitation. The RSSB on behalf of the UK Rail Industry established a cross-industry research program, T1010, to build stronger cooperation between stakeholders in sharing RCM data. This research builds upon T1010, to explore the use of blockchain and smart contracts to automate, in an auditable and tamper-proof way, the commercial agreements and payment processes for data trading. By removing the limitations of paper-based agreements, our goal is to enable innovation in shared business processes and an IoT data marketplace. Building on existing smart contract-based schemes for trading and sharing IoT data over blockchain networks, this research identifies novel ways to enforce agreements and ensure fair cost attribution between parties, without a Trusted Third Party. The initial design of a blockchain-based framework is presented, oriented around the data provider, consumer, and smart contracts. Blockchain-hosted data access agreement and accounting models are specified in detail. The processors in the efficient permissioned blockchain platforms Hyperledger Fabric, Sawtooth, and Iroha have been analyzed for their suitability for implementation. We then outline our future work to evaluate and validate two industrial use cases: monitoring systems for unattended overhead line equipment and axle bearings.

This paper introduces a novel digital ticketing platform using blockchain technology. Taking in a number of considerations by observing legacy ticketing systems and existing attempts at digital ticketing, we make use of IBM's Hyperledger Fabric framework to design an architecture that distributes the tickets across all participating organisations. We note the potential benefits this platform has. Governing organisations maintain their right to set the rules of the platform and access the data to generate statistics. Vending organisations share access to the same underlying tickets whilst preserving competition. The platform offers passengers a variety of ways to pay for and access their tickets, using a combination of legacy and modern methods. Furthermore, we note the platform has the potential to eradicate paper ticketing and surplus voucher cards.

This paper addresses the problem of uploading large quantities of sensitive industrial data to a public distributed network by proposing a new framework. The framework combines the existing technologies of the distributed web and distributed ledger to provide a mechanism of encrypting data and choosing whom to share the data with. The framework is designed to work with existing platforms; the InterPlanetary File System (IPFS) and the Ethereum blockchain platforms are used as examples within this paper, though it is stated that similar platforms are capable of providing the requirements for the framework to operate. The framework uses the concept of the Diffie-Hellman Key Exchange (DHKE), and is implemented in three different mechanisms of the DHKE: one-step Elliptical-Curve Diffie-Hellman Key Exchange (ECDH); two-step ECDH; and Supersingular Isogeny Diffie-Hellman Key Exchange (SIDH). The paper discusses the security of each along with individual advantages and disadvantages, and concludes that the SIDH is the most appropriate implementation for future use due to it being post-quantum secure.

The railways worldwide are increasingly looking to the integration of their data resources coupled with advanced analytics to enhance traffic management, to provide new insights on the health of infrastructure assets, to provide soft linkages to other transport modes, and ultimately to enable them to better serve their customers. As in many industrial sectors, over the past decade the rail industry has been investing heavily in sensing technologies that record every aspect of the operation of the railway network. However, as any data scientist knows, it does not matter how good an algorithm is, if you put rubbish in, you get rubbish out; and as the traditional industry model of working with data only within the system that it was collected by becomes increasingly fragile, the industry is discovering that it knows less than it thought about the data it is gathering. When coupled with legacy data resources of unknown accuracy, such as design diagrams for assets that in many cases are decades old, the rail industry now faces a crisis in which its data may become essentially worthless due to a poor understanding of the quality of its data. This paper reports the findings of the first phase of a three-phase systematic review of literature about how data quality can be managed and evaluated in the rail domain. It begins by discussing why data quality matters in a rail context, before going on to define the quality, introduce and expand the concept of a data quality schema.

This paper describes on-going research developing a system to harvest and utilise open and crowdsourced data related to the UK railway systems. This system will allow the controllers and decision makers to listen to the messages posted on social networks by passengers or other members of the public and relate these messages to specific (physical) trains that are referred to in those messages, by fusing information from other open sources. This will enable the railway controllers to take prompt actions in case of any emergency or simply to improve the quality of customer service.

This paper presents the railway core ontologies, a group of related ontologies designed to model the rail domain in detail. The purpose of these ontologies is to enable improved data integration in the rail domain, which will deliver business benefits in the form of improved customer perceptions and more efficient use of the rail network. The modularity of the ontologies allows for both detailed modelling of the domain at a high level and the storing of instance data at lower levels. It concludes that the benefits of improved rail data integration are best realised through the use of the railway core ontologies.

This paper presents early findings from a larger study, into the use of linked data in the rail domain. The study and other literature has shown there to be benefits from improved integration of data in this domain and proposes that linked data in general and ontology in particular will address this. The paper will set out the current state of data integration in the British rail domain, highlighting issues found there. The manner in which linked data is employed in the broader transport domain will then be examined along with previous work pertaining to the rail domain.