Transport Transitions: Advancing Sustainable and Inclusive Mobility

Considering the significant growth rate of population in the urban area, public transport has become vital to urban living. It has become unavoidable to promote the culture of using Mobility as a Service (MaaS) among travellers to address climatic challenges, especially the global warming phenomenon. To encourage the use of public transport, It is important to introduce innovative IT solutions to the ecosystem of TSPs (Transport Service Providers) backed by an in-depth impact analysis to meet the expectations and the needs of the TSPs and the travellers. This work introduces an assessment methodology to calculate the “Effectiveness” of the innovative IT solutions for TSPs and travellers through a series of data analysis methods using the Bayesian Network, Regression analysis, and ANOVA tests. The assessment is based on two types of quantitative datasets: operational KPIs (Key Performance Indicators) and USI (User Satisfaction Index) surveys to evaluate how the expectations and needs of travellers with different socio-demographic profiles (by gender, age, income level, and impairments) are met by these IT solutions. This methodology is the foundation for the IP4MaaS Project supported by the Europe’s Rail Joint Undertaking. The paper presents the results of applying this methodology to data collected in six sites (Athens, Barcelona, Liberec, Osijek, Padua, and Warsaw). The presented methodology will be helpful to the IT developers and TSPs for assessing their own IT solutions. The findings will help researchers, policymakers, and others in the transport sector to assess public transport services. This assessment methodology is scalable to other demo sites and datasets.

To mitigate the adverse effects of climate change, greenhouse gas emissions need to be minimised. The FlexiDug project investigates sustainable transport perspectives for structurally transforming areas where coal mining phases out. This work presents a safe, economical, and extendable approach on reusing industrial railways for passenger transport. We have digitalised the Zugleitbetrieb, a mode of operation that requires no trackside equipment. Our Train Dispatcher in the Cloud (German Zugleiter in der Cloud, henceforth ZLiC) has been developed ontology- and model-based. It also takes a railway network model as input, e.g., for the generic interlocking logic. Speech to text, naturallanguage understanding, and text to speech recreate the established speech interface towards conductors. A state machine ensures the prescribed voice procedure. Custom voice-activated recording allows using COTS radio devices. ZLiC has been evaluated successfully in simulations and field tests. We plan further improvements, evaluations, and a risk assessment.

Along with much of Europe and the global trend towards in-cab signalling, Great Britain (GB) rail is transitioning to Radio-based European Train Control System (ETCS). In collaboration with several universities and research partners, Network Rail have undertaken a programme of R&D to build on the opportunities that Radio-based ETCS offers, including the move towards automatic train operation (ATO), and integrated, centralised traffic management systems that maximise the potential in capacity, performance and energy efficiency for passenger and freight customers. Bringing the train planning and timetabling capabilities into the modernised, data driven, information rich world is a significant puzzle piece of turning opportunities offered by the signalling and control technologies into the day-to-day operations of the railway. This paper covers the research carried out into the characteristics of a radio-based ETCS railway that can be analysed for a goal-based state of the art train planning capability. It considers the advancement of tools, techniques, processes and skills that are required to plan, operate and regulate the railway through automatic train operations and future traffic management systems, ultimately harmonising planning, real-time operations and post-operations performance analysis.

SNCF Voyageurs/TGV-Intercités operates over 800 highspeed trains (TGVs) per day in France. The system can be highly tense during peaks with up to 13 trains per hour on the same track section. Thus, minor delays can affect operations, especially for long-distance trains. Supervision of the system and real-time rescheduling are difficult tasks given the complexity of the rail network and the cohabitation of different train services.In this work, we present a real-time decision-support tool providing estimations on the arrival time of trains at each station and at destination and helping comparisons between rescheduling choices.The estimations of future delays have a focus on explainability, with information on the causes of the delay and on the possibility to recover the delay. The operators can use the tool to test different rescheduling choices and see the impact of each scenario on the traffic, helping them deciding the actions to take to minimize delays. The predictions of arrival times of trains are based on a macroscopic discrete event simulator, coupling the theoretical timetabling with real-time information on the trains’ positions.Arrival times are displayed on the interface of the developed web application, where the user can interact with the simulation by adding information and comparing different disruption management scenarios.The tool has been tested with success in an operational environment. The operators gave positive feedback on the tool, underlying its capacity to give them more insight on the expected delay evaluations and on potential conflicts. The information displayed was judged relevant and reliable. This is confirmed by our analysis of the quality of the simulation.

Variable speed limits (VSL) are currently being introduced on Ireland’s M50 motorway on a phased basis. To manage general everyday congestion associated with peak-period traffic, proactive speed management plans have been developed to allow control room operators to initiate speed reductions to optimize traffic flows and reduce the impact of traffic flow breakdown. The national roads authority in Ireland, Transport Infrastructure Ireland (TII), collects significant levels of data from the M50, and this paper outlines how the application of traffic-flow theory to the measured data has been used to identify when certain sections of the road are approaching capacity. This has enabled the provision of real-time data-driven alerts to control room operators, to identify when proactive speed management plans should be initiated. The measured data is then used to assess the effectiveness of the VSL plans, and it is shown that improvements in throughput are being achieved, along with a reduction in congestion and shockwave behavior. The findings of this study will ultimately inform the automation of proactive speed management plans on the M50.

This paper aims at defining future research priorities for artificial intelligence (AI) in transport systems. The point of the departure is the state of the art regarding the application of AI in transport combined with the EU policy needs and the assessment of the recent European projects. The paper presents an overview of the potential benefits that the deployment of AI has for transport. Special focus is put on results of European projects funded within Horizon 2020 Framework Programme. The main achievements of the research have shown that AI can help in the optimisation of applications within the transport means. It can also aid public authorities in improving the overall infrastructure thanks to data gathering and first screening. Further, the deployment of AI is a source of profitable opportunities for various sectors like automotive, software, waterborne, aviation, passenger and freight transport. The final aim of these projects is to boost efficiency, sustainability, and safety by having a human centric AI, aware of every situation it is facing and able of taking prompt decisions. The paper concludes by discussing selected cross-cutting concerns regarding the application of AI in the transport sector. They include definition of software updates for autonomous vehicles’ algorithms, the need for liability regimes and the issue of data scarcity.

Strategic planning is of utmost importance for cities to achieve climate targets, while maintaining citizen satisfaction and meeting financial constraints. However, large assessment models are often costly and too complex for smaller cities. Thus, we designed a simplified, intuitive and dynamic transport simulation tool and serious game MobileCity to run on mobile devices for strategy processes, participation and teaching. A real transportation model running on local devices allows the free combination of different mobility interventions and shows dynamic indicators for climate, finances and livability, while offering an intuitive interface with supporting information. We currently extend the free iOS and Android demonstrator for Karlsruhe (Germany) and transfer it to European cities within the DUT project “CarGoNE-City”. In this paper, we present its core functionalities and features, before assessing scenarios and concluding on lessons learned and applications. Our main messages from applying the MobileCity-App are: the timing of interventions matters for all output indicators and a sound combination of push and pull measures helps meeting climate, livability and financial targets without going into extremes.

Improving the efficiency and competitiveness of European Shipyards is one of the priorities of the HORIZON program, funded by the European Commission. The proper use of computational tools can accelerate this improvement, given that the shipbuilding industry faces a digitalization gap compared to other manufacturing industries. Our proposal is based on the ongoing Smart European Shipyard (SEUS) project, which aims to bridge the digital gap, focusing on integrating available computational tools, and converging into a new platform that enables faster engineering and technical management. This platform intends to provide a holistic approach to product lifecycle management (PLM) for shipbuilding, integrating existing and proven solutions in CAD/CAE with new data-driven technologies to handle shipbuilding knowledge efficiently. The paper presents a link between current challenges and possible ways to tackle them and a summary of the possible impacts this platform can achieve if adequately implemented. The paper closes with a call for peers to contribute to the discussion.

CoPropel is a Horizon Europe funded project which aims to develop marine propellers for cargo vessels that are more fuel and cost efficient to operate than their more common metallic counterparts. One of the areas the project aims to tackle is the maintenance aspect of marine propellers, which can be very cost intensive. This is due to the difficulty of inspection or maintenance since the components are underwater and quite significant in size, which may lead to the need for divers or dry-dock time. The way the project aims to address this is by taking advantage of the non-monolithic construction of the composite propeller to integrate a Structural Health Monitoring (SHM) system to monitor the strain, and subsequently infer the condition, of the propeller during operation without the need to stop for inspection. This paper explores the challenges encountered in the development of the SHM system, including: 1.) inspection requirements as set out by existing guidelines and regulations, 2.) sensor integration in the composite structure, 3.) data transmission from a rotating underwater component to a data acquisition system within the ship and 4.) usage of the acquired data for maintenance decision making. Feasible alternative systems are explored including Rayleigh Backscatter based Fibre Optic Systems (FOS) for distributed strain sensing as well as a strain gauge system with wireless underwater data transmission. Preliminary small scale underwater tests are conducted to evaluate the performance of the explored concepts under simulated operational conditions. Finally, we combine the strain data acquired from the SHM system with numerical models of the propeller through a multifidelity approach. The first aim is to create a better spatial distribution of the strain measurements across the propeller by fusing data from sensors covering a limited area, with strain data from numerical models that have better distribution across the propeller. The second goal is to provide digital twinning capability through the use of the numerical model to provide extrapolated estimates (calibrated by live sensor data) of maintenance requirements (i.e. remaining life) based on current and hypothetical operational profiles to better inform operators when making future operational decisions. Through the combination of the SHM system and digital twinning capabilities we provide increased data driven decision making capabilities to better improve operational efficiency and maintenance costs.

An inadequate road maintenance planning coupled with funding constraints, traffic volume rising and lack of data, results in aging pavements with increased fuel consumption and diminished user safety as well as exorbitant corrective preservation costs. Assessing road conditions for pavement distress detection currently involves two approaches: human visual inspection, which is labour intensive and time-consuming, and the use of multipurpose pavement inspection vehicles. Notwithstanding, the latter is expensive to purchase, operate and maintain, which may pose challenges for departments of transportation with limited budgets. This project automates the process of recognizing superficial road defects with an innovative solution based on Artificial Intelligence, thereby adding significant value to road rehabilitation decision-making. Our low-cost image acquisition system collects thousands of geotagged road images. Then, multiple Deep Learning (DL) algorithms belonging to the YOLOv5 family are trained to establish a functional mapping between the inputs (raw images) and the outputs (defect location and type). Also, validation metrics are calculated in order to identify the optimal DL architecture. Subsequently, a rule-based postprocessing is devised for the finest model, with the goal of mitigating false positive detections. The enhanced model outputs are utilized to engineer a pavement condition index, which is integrated in our software.

Road pavements are vital for transportation infrastructure, yet they deteriorate over time due to traffic loads and environmental factors, resulting in cracks and damage. This paper introduces an innovative method for crack detection on road pavements using digital imagery. Our approach incorporates geo-localization, annotates, characterizes, and quantifies crack severity. This empowers experts to monitor crack progression, a critical element in pavement management. The methodology allows for seamless result comparison and augments existing techniques, aiding in condition assessment and conservation strategy determination. Timely detection of cracks enables proactive maintenance, preventing structural degradation, and ensuring user safety and comfort. Leveraging deep learning and open-source frameworks like TensorFlow and QGIS, our approach automates road pavement image analysis and crack identification, providing a cost-effective, accessible solution for crack detection. This research offers significant advantages in resource efficiency and accessibility, especially in areas without regular manual inspections or dedicated vehicles, thereby enhancing road pavement monitoring and maintenance.

The OMICRON project develops an Intelligent Road Asset Management Platform integrating a broad portfolio of area specific innovative technologies to enhance the construction, maintenance, renewal and upgrade of the European road network. The project improves the whole asset management pipeline focusing on four pillars: modular construction of bridges, road inspection digitalisation, predictive maintenance and smart execution of intervention actions.OMICRON’s Intelligent Platform enables the digitalisation and automation of a relevant portfolio of road management tasks which are still very labour intensive. Thereby, OMICRON aims to pave the way to the roads of the future, addressing the reduction of fatal accidents related to maintenance actions, the reduction of traffic disruptions, the reduction of maintenance costs, and the increase in road network capacity.

Geofencing is a tool that offers innovative solutions to manage and control traffic, transport, and mobility. The technology enables cities to define digital zones and to create dynamic rules for mobility within these zones. Here we report on three geofencing use cases, their results and lessons learned that were conducted as part of the joint European project GeoSence. In the city of Gothenburg, the performance of a geofencing-based retro-fitted intelligent speed assistance system was tested and evaluated in 20 vehicles of publicly procured transport services to support drivers in complying with new speed regulations around schools. In Munich, geofencing was used to implement and enforce a new station-based parking regulation for shared e-scooters in the city’s old town. Thirdly, in Stockholm preconditions, processes and workflow for continuous changes and updating of the underlying digital geo-data bases were analysed to better understand institutional and practical challenges to implement geofencing-based digital transitions in future. Findings from the use cases and project accompanying surveys are evaluated regarding the general topics of transport management, including issues of data sharing and management, stakeholder involvement, technical & vehicle readiness, feasibility of technical platforms, as well as institutional problems related to governance, policies, resources and the acquiring of necessary competencies.

The transportation sector is undergoing a profound transformation, utilizing digital technologies to move people and goods more efficiently. Data and analytics play a pivotal role as the core of digital transformation and insights-based decision making, as organizations are realizing the necessity of effectively leveraging their data assets. This paper discusses how advanced data analytics techniques, such as artificial intelligence, and solutions can be harnessed to embrace digital innovation and improve operations and services while respecting the transportation industry’s unique requirements regarding stakeholder engagement, user needs, supply chain, legacy systems, stringent safety and security regulations, and system interoperability. This discussion is built around project examples: a Digital Engineering Information Management solution for managing large and complex road construction programs; natural language processing on traffic incident data; a telematic assessment of fuel consumption and emissions from road traffic; and the predictive maintenance of transportation infrastructure. This paper concludes with the proposal of a systematic framework that encourages best practices, clarity, efficiency, and successful outcomes and value extraction from data analytics projects for the transportation sector.

As a new digital tool, Mobility as a Service (MaaS) provides the opportunity for novel approaches in delivering policy outcomes when led by public authorities. Harnessing the opportunity offered by MaaS requires not only a behavioural, operational and commercial transition for public bodies, but also a digital transition. Whilst complex, this transition opens up opportunities. Collaborative digitalisation means recognising that participating actors and stakeholders can often have competing motivations and objectives, reflecting the complexity of delivering MaaS as a system of digital and commercial relationships. However, with the right behaviours and frameworks, all parties can align towards a common goal. This cannot be achieved by chance, but only through concerted and considered effort to develop a culture of collaboration. This paper explores the evolving collaborative delivery model being taken by the West of England Combined Authority in its digital transition to deliver a policy led, customer focussed MaaS solution.

To achieve a 90% reduction in the transport sector’s greenhouse gas emissions by 2050, the North Sea Region has to move (among others) from fossil fuelled vehicles to cycling, especially in urban areas. All kinds of projects are initiated to contribute to this objective, each with their own objectives and means. The MegaBITS project, and its predecessor BITS, are aiming at greening the European transport system through the use of Intelligent Transport Systems (ITS) in the cycling domain. ITS can contribute to the attractiveness of cycling, by improving cycling safety, improving speed, improve convenience and comfort and by giving the cyclist a better experience. This paper highlights the results of the BITS project, shows the first results of the MegaBITS project and sheds a light on the prospects of smart cycling.

Nowadays passenger travels are more and more multimodal. Often advocated as the way to follow to strike a balance between sustainability and travel time, air-rail is a prominent example of multimodality. To accommodate the fast-growing business the travel and tourism industry is experiencing, operators are going through a continuous digital transformation including solutions based on wireless connectivity, smart-sensors, Internet of Things, and Artificial Intelligence. The goal is twofold: improving passenger processing and operations, and passenger experience. On the other hand, significant challenges to tackle and opportunities to unfold in terms of security, privacy, and optimization, are still only partially addressed. The EU H2020 E-CORRIDOR project develops a secure, collaborative, and confidential framework for information sharing and analysis. Solutions for the air-rail multimodal pilot seek to improve passenger experience, perform seamless authentication, enhance the cybersecurity posture of the transport operators, break down data silos existing among stakeholders, and better support Passenger with Reduced Mobility (PRM) and coordination among operators. This paper presents some of such solutions where privacy is the keystone.

Origin-destination (OD) matrices from floating phone data (FPD) are a valuable data source for screen line analysis. However, FPD does not contain any trip purpose information. To get additional information regarding the trip purpose distribution at a screen line, manual surveys with a huge sample size would be needed. Such surveys are very complex and expensive. Hence, they are rarely conducted in practice.This study aims to overcome these shortcomings and presents a novel approach for assigning trip purpose distributions to OD-matrices. Based on limited but geocoded survey data, spatial structural data and routing information trip purpose distributions can be mapped on OD-matrices from readily available FPD. By using k-means based clustering techniques the spatial structure is used as a link between FPD and survey data.The developed methodology was successfully applied to the Graz region in Austria, with approximately 300.000 residents and showed promising results. The derived trip purpose distributions were verified by utilising traditionally survey data. Therefore, this method is transferable and can be used as supplement to traffic volume screen line analysis to gain valuable planning information while keeping the survey effort and related costs to a minimum.

Networked production, supported by advanced logistics and supply chain processes, is crucial for companies to stay competitive and foster cooperation and integration of production resources. It replaces sequential processes with dynamic arrangements, presenting challenges like managing product variants, short life cycles, and process optimisation. Agility is vital for adapting to changes and natural disasters. Decentralised Technical Intelligence (DTI) is an approach that manages complexity and incentivises integrating new technologies in planning and manufacturing.DTI involves distributed and autonomous intelligence embedded in interconnected systems, where humans and machines collaborate to achieve common goals. Humans bring unique skills like creativity and intuition, complementing AI’s capabilities. DTI relies on a multi-agent architecture, enabling trust, interoperability, and data sharing for better decision-making and efficiency. The EU knowlEdge project exemplifies this by providing AI solutions that are distributed, secure, standardised, and collaborative, integrating cognitive technologies, data analytics, IoT and more.DTI’s human-centric design fosters a different quality of intelligence, leading to greater autonomy within multi-agent systems. To realise advanced networked production, a roadmap must be implemented, focusing on a vision, value promise, and development pathway. Europe can maintain its leadership in future networked production through this approach.

The digitalization of the railway domain is a key enabler for more efficient railway operations matching the future needs of society. One part of this process is the digitalization of planning processes, replacing the use of paper-based plans with digital formats. Digital planning documents also open new possibilities, e.g., deriving representative simulation in early project stages. A commonly used simulator for such purposes is the Simulation of Urban Mobility (SUMO). To use the capabilities of SUMO, we are presenting a tool chain for unifying planning documents and generating simulation configurations from them. The core of this tool chain is the yaramo model, covering mainly the topology, geography, and the control command and signaling (CCS) infrastructure of the railway network. The tool chain consists of three major layers: Importers to support multiple data sources (such as PlanPro or OpenRailwayMap), processors to enrich the model, and exporters to support various consumers of the model. This leads to several applications, such as rail network performance evaluations and test automation for CSS infrastructure. Ultimately, our work aims to support the digitalization process of the railway domain, especially the digital planning and development of railway networks.

An interactive tool for strategic planning is developed as part of the decision support and operational planning enhancements in the framework of the In2Smart2 project [1], working with Strukton Rail as the technology demonstrator.The tool objective is to assist workers which form part of the decision process of strategic planning to visualize and analyze information, providing insights and useful conclusions in an interactive and straightforward way. The tool scope is the optimization of resources and company availability analysis to obtain forecasted workload optimization, the reduction of idle times and mobilization of adequate volumes of extra (external) capacity among others. It can be applied to a long-time horizon and to a short one.The tool is implemented in Powe BI, elaborating methodologies and workflows for the implementation of the various interactive reports which support the decision-making and other managing and organizational tasks.

The traffic flows monitoring in a specific area is often an expansive and complicated activity for a public administration. Nevertheless, these data are fundamental for improving the urban and transport planning processes at local and regional levels. In the Italy-Croatia Interreg Mimosa project, an innovative opensource traffic flows monitoring system based on Artificial Intelligence (AI) was crated and tested. The collected results showed as the system is able to correctly recognize different typologies of vehicles (cars, bicycles, motorbikes, persons, light freight vehicles and heavy freights vehicles) using only open-source libraries. The aim is to provide to the decision makers new tools for the traffic monitoring and data-oriented decisions making on the topic of sustainable transport promotion. The Mimosa AI tool was published on GitHub and it is open access for all the interested public and private stakeholders. The paper presents the informatic architecture adopted, the key open-source AI tools used and it explores strengths and weaknesses related to the use of this AI tool.

To achieve our long-term goal of doubling the modal share of freight transport by rail and develop multi-modality in transports in France, a fine control over the railway network management is required to better synchronize rail transport with other modes of transport e.g., cars, trucks, trams. The Digital Twin (DT) of the railway system and the extended enterprise are central concepts in our approach to answer this problem. Our method aims at giving access to our railway system's Digital Twin through a Service Oriented Architecture (SOA). This architectural choice facilitates seamless communication and interaction with our partners, fostering a dynamic exchange of information critical for effective multimodal transportation planning. This article describes our approach with a successful implementation of an initial version of our infrastructure Digital Twin, complemented by services designed to meet the diverse expectations of users. This milestone underscores our commitment to leveraging cutting-edge technology and collaborative frameworks to enhance railway management, promote sustainable transportation practices, and contribute significantly to the reduction of GHG (Greenhouse Gas) emissions. As we continue to refine and expand our Digital Twin capabilities, we remain dedicated to advancing the future of intelligent and eco-friendly transportation systems.

Digital Product Passports (DPPs) have increasingly gained attention as enablers of more transparent and traceable electric vehicle battery (EVB) value chains. The new EU Battery Regulation requires all EVBs to have a digital record until 2026 and defines sustainability/ circularity-related requirements to be included in such a digital battery passport (DBP). This study goes beyond the legal requirements by systematically investigating which type of information a DBP would need to contain to comprehensively support sustainability/circularity-related decision-making of value chain actors. This is done by a mixed-methods approach divided into two phases: 1) a literature review, an industry actor survey and an EV user survey, and 2) two sets of interviews with experts from the battery’s End-of-Life (EoL) and Battery Second Use (BoL) phases. The results consist of a refined and prioritized overview of information requirements that are of particular relevance for EVB value chain actors. They also allow for a differentiated insight into the requirements of specific use cases from the EoL and BoL stages. The paper thus serves as the foundation for developing a DPP prototype and for conducting sustainability assessments by providing a holistic perspective on EVB value chain actors’ battery management and data requirements.

This paper provides an overview of the main results achieved within the Horizon 2020 Shift2Rail project named RAILS (Roadmaps for Artificial Intelligence Integration in the Rail Sector). The RAILS roadmapping process provided state-of-the-art, taxonomy, future research directions, and recommendations in three macro areas: Railway Safety and Automation, Predictive Maintenance and Defect Detection, and Traffic Planning and Management. RAILS findings shed light on the potential of intelligent technologies and provided essential guidelines for integrating machine learning into next-generation smart railways.

The TANGENT project ( www.tangent-h2020.eu/ ) aims to address the challenges of urban transportation, including traffic accidents, greenhouse gas emissions, and congestion. The project focuses on optimizing traffic management and enhancing mobility through a distributed, modular, and scalable architecture. TANGENT collects and harmonizes data from various sources, including sensors, users, vehicles, schedules, pricing, and traffic flows. It uses this data to create enriched information for different transport stakeholders. The project combines technologies such as data gathering, travel behavior modeling, traffic prediction and simulation, and transport network optimization to provide advanced transport management services. This paper is focused on presenting the project architecture developed to implement four services: data collection and harmonization, enhanced information service, real-time traffic management, and transport network optimization. The project involves a consortium of organizations from nine European countries and aims to pilot its integrated tool in multiple cities in 2024.

In recent years, advances in Distributed Acoustic Sensing (DAS) technology have resulted in significant progress in the detection of vibration sources. However, its use in railway monitoring is still relatively new, even though thousands of kilometers of optical fiber cables are already set up for telecommunication purposes, thus potentially exploitable.In this paper, we explore the possibility of using a DAS system and machine learning tools to detect rail defects along the track. Rail defects are defined as anything other than a smooth rail, and we focus on the detection of rail joints, which are common elements along the track. In this study, measurements were carried out on a short railway section of a few kilometers between two train stations in Paris. The results show that nearly all rail joints along the track are correctly detected, demonstrating the ability of the system to detect these elements with a spatial accuracy of a few meters. Lastly, some future perspectives for the study are proposed, such as a more in-depth analysis of the detected locations or the integration of field information to enhance the reliability of detections.

Satellite-based localization solutions are expected to boost railway digitalization and in particular, they will enhance evolution and efficiency of railway signaling systems. The development of multi-sensor solutions is ongoing, but some gaps remain. This paper addresses two of them: the need for innovative high accuracy and precision Ground Truth and Digital Maps, essential elements of a EGNSS train positioning system and a V&V environment. These two objectives are focused in the RAILGAP EU project. For each of these tools, this paper presents the main high-level requirements and the selected architectural design exploiting specific data fusion algorithms. The novelty of the EGNSS multi-sensor solution proposed is that it does not require to install or modify any equipment on the track. It is based on datasets acquired through commercial runs in Italy and Spain, leveraging on regular train trips in different operational scenarios and time.

Implementing fiber optic sensors (FOS) in asphalt pavements provides a wealth of data with multiple applications. Successful integration of FOS into asphalt pavements depends on two key requirements. First, the sensor embedded in the asphalt must withstand the paving process without damage. Second, the cable must neither compromise the performance nor the durability of the asphalt.These requirements were rigorously evaluated in a joint effort between the Technical University of Darmstadt and RINA Consulting S.p.A. Using realistic forces and material temperatures, asphalt samples were compacted and cable functionality and integrity were non-destructively evaluated. Standardized mechanical tests were used to conduct asphalt performance under dynamic loading. Void distribution within the asphalt specimens were evaluated using asphalt petrology techniques.Results confirmed the integrity of nearly all cables tested, with minimal impact on asphalt void content and structure. Mechanical tests provided insight into the durability and performance of FOS-containing specimens.

The railway sector brings together heterogeneous industry bodies and actors to design, build, and operate railway systems. The digitalization of the rail sector is supported by numerous technologies and the abundant developments go beyond the organization level and require coordination at the sectorial level. As French railway infrastructure manager, SNCF Réseau has launched the innovative research and development project MINERVE. This project brings together the main players in the French rail sector to build a collaborative ecosystem around railway infrastructures challenges and to develop a shared vision of digital continuity. The challenges addressed in MINERVE are related to the transition towards a more efficient, reliable, and environment-friendly railway operation by designing and building with collaborative digital methods and tools. The main objective is to reduce the overall cost and impact of the railway system while increasing collaboration between stakeholders.MINERVE outcomes are related to specific, standardized, and interoperable methods and tools, for all technical fields, and that can be implemented and adopted by all stakeholders. The MINERVE project is a one-of-a-kind project to unlock digital collaboration at the sectorial French industry level to improve the life span of the infrastructure as well as the environmental performance of railway projects including biodiversity and resilience to climate change.

This paper presents a schematic plan outlining a methodology for determining train positions using digital twin technology and commercially available positioning sensors. The plan involves an offline step and an online step. The task in the offline step includes: building a library of 3D models for objects on railway tracks, and constructing a real digital twin environment. Then in the online step, train positions can be accurately determined by automatically generating real-time semantic point clouds from images and matching them with the 3D objects in the digital twin. Along with these tasks, a City Geography Markup Language (CityGML) Application Domain Extension (ADE) for modeling railway tracks in 3D will be developed and suggested as Open Geospatial Consortium (OGC) standard for similar applications. All these procedures will be conducted in a controlled environment using remote-controlled cars before being applied to an actual railway track. It is hoped that this localization methodology can enhance traditional positioning methods, ultimately leading to improved network operation and maintenance.

Smart factory research has primarily concentrated on the manufacturing sector, without taking the building sector into closer consideration. Combining these two ventures can lead to a variety of synergies to minimize the CO2 emissions of existing buildings. By producing facade and roof panels on an industrial scale for energetic refurbishments, Construction 4.0 and prefabrication can make a significant contribution to achieving climate targets. In this context, the purpose of this paper is to identify, how data of a virtual building can be used in procurement logistics of a smart factory. To achieve this, this paper follows a case study analysis. At the beginning, the procedure of developing a virtual building model is described. This is followed by the design of the inbound workflows of a smart factory in the construction industry. The findings demonstrate how the information from virtual building models can be leveraged to control procurement processes of a smart factory.

Ships generate huge amounts of sensor data, which is largely underutilized due to lack of standardization. The ad-hoc data collection and transmission procedures and proprietary nature of sensor naming conventions make it challenging to implement and scale digital solutions based on this data. Ensuring data integrity can present an additional challenge as data is generated by multiple sensors onboard a ship with constrained connectivity to shore. Sensor as well as stakeholder identities are significant here, in order to reflect ownership structures, maintain access control and link actions with the entity that performed it. In this paper we present Vidameco, an integrated hardware and software framework for standardized data collection and tamperproofing. Dockerized microservices are developed for data collection, hashing, publication of hashes on the blockchain and verification by data consumers. Further, we present an architecture to integrate this tamperproofed data with sensor and stakeholder identity in order to create an underpinning for a digitalized value chain. Sensors are named in a hierarchical structure using the ISO19848 standard and this structure is interfaced with digital identities of stakeholders on the blockchain such as from the European Blockchain Services Infrastructure (EBSI). Finally, taking EU’s Monitoring Recording and Verification (MRV) regulations as a case study we conceptually demonstrate this framework. MRV reporting is a complex value chain involving multiple stakeholders and extensive documentation and forms the basis for EU’s Emissions Trading System (ETS), which is set to include shipping from 2024. This case study presents and evaluates an end-to-end digitalized ETS ecosystem based on tamperproofed data using MRV as the Oracle in smart contracts for ETS trading.

The Flagship Project “MOTIONAL”, supported by Europe’s Rail initiative, paves the way for implementation of the future European Capacity Planning and Traffic Management System, built on digitalisation, automation, connectivity and multimodal integration.Lead jointly by Hacon and Trafikverket “MOTIONAL” with its 89 partners aim to achieve a significant advancement in the state-of-the-art railway systems by pursuing the way to digitalization and interoperability across Europe. To achieve the expected results activities are carried out in two different working domains. The first domain covers three focus areas; planning, operational activities - which includes managing future interactively coupled timetable planning; and finally operational traffic management systems - which also includes integration activities for door-to-door mobility. The second domain refers to the delivery of a set of digital enablers for all European rail destinations, i.e., crosscutting, to support the development of destination-specific digitalisation solutions. The main outputs of the project will be significant improvements to the railway systems in Europe through the development and implementation of a range of technical enablers. The results of FP1 MOTIONAL will enhance the strategic and tactical capacity planning of the network by enabling a seamless cross border planning and integrated yard and station capacity planning and using modern optimisation and simulation technologies.

The research objective is to contribute to more effective and greener road freight transport with HV’s, without damaging aging road infra below. One of the best solutions in 21st century is to use all kinds of digital data (temperature; GNSS; OBW etc.) and road maps to control the logistics in the most optimum way, depending on the used vehicle’s load type (Intelligent Access IA). It’s a very cost-effective system. Since the HVTT15 & HVTT16 conferences, much work has been done in Estonia in the logistics digitalization area. In Transport Administration the e-waybill has been piloted to become in 2024 as a compulsory measure in building contracts for the greener infra buildings. Estonian Ministry of Climate has developed the architecture and started to pilot eCMR and eFTI in wider scale and also more widely all over Europe.

Precise travel and traffic information are becoming increasingly important to road users. This is backed up by a growing number of users who are planning their trips before they start their journey. People primarily aim to avoid or minimize the travel delays by knowing alternative routes or changing the departure time beforehand. Furthermore, they are increasingly customizing their travels to maximize efficiency and convenience. From years of experience, and considering the Traffic telematics report [1], ASFINAG, as the national operator of Austrian motorways and highways, has recognized this need and has focused on bringing the accuracy of our traffic information to real traffic conditions. In this paper we present our hybrid approach that combines two methodologies to provide traffic information that is closer to the real traffic conditions, with a special focus on the traffic jams and delays. Our main goal is to improve the accuracy of the travel delays and traffic jams both in terms of time and location. The first methodology, which is a fusion of data from various data sources, is based on pure statistical model, whereas the second methodology is a machine learning (ML) model. This hybrid approach is not a comprehensive one but rather restricted to selected road sections.

The Future Mobility Campus Ireland (FMCI) was founded with the sole purpose of creating and delivering future mobility testbed facilities for stimulating research, development and innovation in the area of Autonomous Connected Electric Shared Vehicles (ACES), including Connected and Autonomous Vehicles (CAV) and Advanced Air Mobility (AAM), in Ireland. FMCI focuses on comprehensive mobility technologies that span both ground (autonomous driving, micro-mobility, smart cities, V2X communications) and air (unmanned drones, eVTOL, AAM, UTM) uses.FMCI supports a range of parties, from individual researchers to multi-national corporations, as well as start-ups and government entities. FMCI assists organisations to conceive, develop, trial, and deploy societally transformative transport solutions both nationally and internationally.The installation of state-of-the-art connectivity technologies at the FMCI testbed is providing an environment for the automotive industry, component suppliers, telecommunications companies and research institutions with the opportunity to adopt new approaches to develop innovation for future technologies. FMCI has been operational for over a year and is already facilitating various companies and higher education institutes (HEIs) to further their innovation and/or technologies.

Despite increasing digitalization, rail operators and other mobility service providers often need more real-time information regarding the occupancy of trains and stations. Worse, only general assumptions regarding passengers’ specific destinations, individual needs, and current and past experiences can be made. Digital Twins of Travelers (DTT) address the issue by holding real-time digital representations of passengers throughout their journey from start to destination. Being informed about a passenger’s route instead of isolated sections enables customized real-time forecasts, accessibility information for each transfer location, and veridical updates about how much time may be spent there. For example, a traveler would benefit directly when parts of a journey must be rescheduled due to delays on the original route. When continuing with means of transportation different from those chosen initially, forecasts and other information can be immediately adapted to the new route. In the same scenario, mobility service providers would benefit by being informed about occupancy changes, allowing them to prioritize scheduled connections if necessary. DTT consist of a real-world twin, its digital counterpart, and an interchange component linking them. The digital twin represents how travelers experience current events, holds information regarding their individual itineraries’ progress, and can exchange information with mobility service providers. DDT's purposes range from supporting passengers in real-time by providing information relevant to their journey milestones, analyzing the effect of events such as delays on the travel experience, to helping to align transport services with the existing demand.

Maintenance, traditionally perceived as a reactive cost and a hindrance, poses challenges to efficiency when components succumb to unforeseen breakdowns. In addition to the financial implications, the repair process also incurs substantial time wastage. To overcome these obstacles and achieve enhanced efficiency and cost savings within the manufacturing sector, this paper presents a conceptual study of a technologically advanced predictive maintenance (PdM) approach, particularly in the realm of artificial intelligence-powered digital twins. The effectiveness of these solutions hinges on their data-driven nature, technical feasibility, and acceptance by industry stakeholders.

The EU-funded SOLUTIONSplus project, which aims at kick starting urban e-mobility in developing countries, included the provision of a white label app customizable to the needs of the cities as part of its offer to its 7 demonstration cities in Asia, Africa and Latin America. Despite having the possibility of testing the customized app free of charge for the duration of the project, only 2 out of 7 cities, Quito (Ecuador) and Kigali (Rwanda), started and continued the process. Yet, only Quito was able to test the customized app in real operations. Thus, the paper analyzes the MaaS level and implementation barriers of the 7 cities and conducts an in-depth expert assessment of the technology, organization and environment enabling factors (TOE) to implement the MaaS concept in Quito, Kathmandu and Kigali. The results show that despite some progress towards an intelligent and integrated transport system in the analyzed cities, an important number of conditions that are a given in the Global North (e.g.: formality and integration of PT system), still need to be met in cities in developing countries before MaaS could be realized.

In this paper we describe the results of a study on the design of open and co-opetitive systems of systems for mobility. A system of systems (SoS) is a set of independent systems (CS – Constituent Systems) that interact to create capabilities that none of the constituent systems can accomplish on their own. A CS can be simultaneously part of several SoS. The independence of the constituent systems is an important and central ingredient in an SoS, and is often divided into managerial and operational independence.We argue that the mobility SoS of the future can benefit from being open and co-opetitive (i.e., simultaneously collaborating and competing) to enable combined mobility (MaaS) for real. If competition between CS is not allowed, new entrants will choose to compete with the SoS instead of becoming part of it. If a new entrant develops much better vehicles or better ways to find transport solutions for users, that entrant must be able to become part of the SoS and take market share from other actors.

All major players in road operation and traffic management of Austria joined forces in the EVIS.AT platform and developed a harmonized real time traffic information network. It provides a countrywide, comprehensive, and authorized real-time data basis for traffic information, traffic management and traffic analysis. The platform delivers traffic messages for planned and unplanned events as well as for traffic regulations and access restrictions and traffic flow data such as current and predicted speeds and LOS. This is done for all major street levels (highways to rural roads) for all of Austria and is based on sensors, probe data and journalistic data of the authorities. The involved parties committed to long-term operation and provision of real-time data through a public-public cooperation. Thus, the platform EVIS.AT serves as strong basis for road-related traffic data in the context of integrated traffic management as well as comprehensive traffic information. The authorized real-time road data of EVIS.AT is combined with public transport data of all operators, including timetable, real-time and message data in the intermodal routing platform VAO.AT. The platform delivers high-quality intermodal routing and traffic information services, based on the comprehensive data sets of Austria’s various public authorities. It is used by partners and b2b customers to create powerful end user applications as well as various tools for administration.The paper reports on objectives and status for these platforms, what makes them unique in the European data and service landscape and how these data and services are being used in national and international applications.

This paper introduces a formal metadata specification for mobility data portals as an extension of DCAT-AP, called mobilityDCAT-AP. It addresses a scenario in which mobility data is offered on a data portal, and is intended to be found, assessed and reused by data users. Unlike in other domains, a structured and community-based metadata for the wider mobility domain has not been established yet. With such specification, an agreed usage of metadata among different portals; easier access to mobility data; improved interoperability in the mobility data eco-system; and the leveraging of semantic technologies are envisioned. In addition, the Resource Description Framework (RDF) as a de-facto standard for metadata, is applied to model the metadata vocabulary. The paper elaborates on the overall goals, previous works on metadata specification and harmonisation, the working process, concrete deliverables and future prospects of mobilityDCAT-AP.

PRESORT is a research project funded by the Conference of European Directors of Roads (CEDR) through the CEDR Transnational Road Research Programme (Call 2022). The aim is to improve the use of third-party data by National Road Authorities (NRAs).NRAs are seeing increasing levels of digitisation and are open to the potential opportunities through the utilisation of third-party data – that is, data aggregated from multiple sources – to support them in the delivery of their core business services in traffic management, asset management, and construction.PRESORT will eventually deliver practical, implementable, easy to use online guide to support NRAs to make better decisions regarding HOW and WHEN to acquire and use third-party data, which will assist their core business activities.The initial phase of this project involved capturing the current state of third-party data use by NRAs. Part of this phase explored the challenges and barriers NRAs face regarding use of third-party data through a literature review and engagement with data providers and crucially, the NRA end-users. This paper reports on the results of this engagement.

In the European vision of a free movement of people and goods, it is obvious that traffic management strategies must not be limited to national borders. Due to the fact that Austria is a small country within an important transit area it is necessary to have a close cooperation with the neighbouring countries. Data is an important basis for modern traffic management, and it became apparent that motorway operators already have many relevant management strategies available. Therefore, ASFINAG has ever since pursued the coordination and harmonisation of Member-State specific approaches, on at technical and organisational level, by aligning topics such as traffic information and management at cross-border level.The ASFINAG solutions are in principle quite easy with mainly list views beside map based information delivered. Therefore similar operators might have no problems in establishing a similar service at low costs as well as in short term. However the content delivering backend systems need to be at hand in order to fuel the customer service.

This paper presents the gateway of the domain-specific Knowledge Graph (KG), which was built based on related data sources and by exploiting the OpenAIRE ecosystem and EOSC services. These enabled it to offer services for integrated KG smart browsing based on impact and reproducibility using AI by also serving several categories of stakeholders. Following the current trends and stakeholders’ needs, the areas of highest interest were identified and gaps in data and knowledge were also detected. On top of that knowledge space, transport research-inspired information retrieval scenarios were implemented by tuning the use of individual AI-assisted services or combinations of them. More specifically, scenarios were built around different use case directions including reproducibility/reusability reports for publications and/or datasets in the transport research sector which were automatically identified and they included information about the ease to reproduce/reuse them and the extent to which the work has already been reproduced by meta-analysis studies.

Railway systems are known as Safety Critical Systems (SCSs). In this kind of system, safety measures derived from the dysfunctional analysis used to be expressed in an informal way. This latter has several gaps in the context of the one going numeric transition: in the early phase of SCSs design, there is a need to link these safety measures to main safety goals. A first step provides a knowledge structure, where the considered knowledge is composed by a set of data and a set of engineering rules. These rules, including safety measures, correspond to a knowhow built through information sharing between actors during previous industrial system life-cycle. From this structured knowledge, models using main concepts can be designed. As concepts come from ontology, the system models are naturally high-level ones and directly linked to the source needs. Indeed, source needs are expressed on the basis of the structuring concepts of the ontology. Obviously, obtained models are abstractions of the real systems. Model based system engineering (MBSE) allows a systematic reasoning and tooled conformance checking and it is possible to assign a meaning to measured data during the whole life cycle of the railway system. A fundamental assumption is the validity of models used during this life cycle. As an abstraction is a partial point of view, the relevance of this partiality must be monitored during the system life cycle in order to avoid ambiguous interpretations. In this paper, the semantic interoperability is tackled to avoid ambiguities and to ensure the railway digital continuity.

Estimation of train delays is crucial for customer information. One cause of the train delays can be easily blamed on the weather. The effect of weather on signaling and dispatching can be indirectly articulated from the arrival and departure delays at the stations. The study uses Norwegian delay data from 2021, 2022 and parts of 2023. This data contains scheduled and actual departure and arrival times of trains on the Dovre line between Oslo and Trondheim. This article talks about acquiring freely available weather data using APIs at the stopping station and checking the effect of weather elements on the departure delay. Weather elements correlated with the departure delays were rainfall (precipitation) and temperature. This study attempts to articulate the quantitative nature of the effect of these weather elements on the departure delays of the trains. The delay prediction model uses different neural network algorithms. The prediction results from different algorithms are compared to provide a deeper insight into the effect of weather on the delay characteristics.

Cycling promotes a healthier lifestyle and alleviates traffic congestion and air pollution. The present study focuses on the dockless bike sharing system in Dublin, Ireland and identifies the socio-economic and built environment factors that affect the dwell time. The origin- destination and timestamp databases obtained from Bleeper Bike are used to estimate the dwell time. The data is analysed using Uber H3 hexagonal zones, and non-spatial and spatial regression models (both spatial lag and spatial error models) are developed. The spatial error model was found to provide a better fit to dwell time. The study identified that key factors such as the presence of public transport stations and car ownership impact dwell time significantly.

Data interoperability is a challenging objective to enable different stakeholders to communicate and exchange information effectively and with unambiguous meaning. Indeed, stakeholders adopt different (legacy) systems for managing and exchanging data that cannot be directly integrated. The H2020 TANGENT project needs to face data interoperability issues to enable the development of innovative tools for multimodal transport network management and for optimising traffic operations. This paper describes the design and the development of the TANGENT solution for data harmonisation and fusion, realised to face heterogeneous interoperability issues related to data discovery, access, harmonisation, integration, and extraction.

As vehicular connectivity and digitisation surge, escalating data transmission to Original Equipment Manufacturers (OEMs) and public authorities is vital for the digital transition, for applications from legal compliance to traffic management. Amidst burgeoning data-sharing ecosystems, ensuring secure, private data transmission and General Data Protection Regulation (GDPR) compliant user control over vehicle identity and data-sharing permissions becomes pivotal, barring legal and enforcement exceptions. This research explores employing blockchain technology to safeguard privacy and security within a Vehicle Identity Management system, using CO2 emissions monitoring as an exemplar. Utilising an emulation-based environment, replicating vehicle interactions with European authorities and the European Commission (EC), the study demonstrates that blockchain systems, specifically for CO2 emissions monitoring, can meet transaction rate and latency demands of large-scale transport applications, accommodating 280 million vehicles reporting annually. This inquiry not only amplifies understanding of blockchain’s applicability in connected transportation systems and secure data exchange among vehicles, authorities, and stakeholders but also lays groundwork for future advancements in trustful, efficient, and secure data interchange, potentially benefiting authorities, industry, and end-users alike.

Data plays a pivotal role in modern mobility research, and data labs have emerged as powerful platforms for promoting collaboration, innovation, and data sharing in the mobility sector. This paper presents a comprehensive methodology designed to plan and execute data labs in the mobility sector, along with its successful implementation within the EU-funded MobiDataLab project context. Central to the methodology is the identification of critical stakeholders, the precise delineation of objectives and challenges, ensuring alignment with genuine mobility issues. It staunchly adheres to a user-centric approach, actively engaging end-users, entrepreneurs, developers, researchers, start-ups, and SMEs with data expertise through a systematic, replicable strategy. Additionally, this paper seeks to introduce the technical infrastructure that was designed and deployed to facilitate these endeavors. The methodology emphasizes the establishment of robust data management systems, protocols, and privacy safeguards. Simultaneously, the methodology advocates for the promotion of interoperability standards and open data formats to facilitate seamless access to diverse data sources. Advanced open data catalogues and data enrichment processors, with anonymization features, further enhance data collaboration and privacy. Capacity-building initiatives enhance stakeholder skills, supported by an Open Knowledge Base for sharing best practices. The methodology's efficacy is finally illustrated through case studies which underscore the concrete benefits of data labs in advancing formal innovation and collaboration within the mobility sector.

Mobility-as-a-Service (MaaS) is a paradigm that encourages the shift from private cars to more sustainable alternative mobility services. MaaS provides services that enhances and enables multiple modes of transport to operate seamlessly and bringing Multimodal Intelligent Transport Systems (M-ITS) closer to reality. This requires sharing and integration of data collected from multiple sources including modes of transports, sensors, and end-users’ devices to allow a seamless and integrated services especially during unprecedented disturbances. This paper discusses the interactions among transportation modes, networks, potential disturbance scenarios, and adaptation strategies to mitigate their impact on MaaS. We particularly discuss the need to share data between the modes of transport and relevant entities that are at the vicinity of each other, taking advantage of edge computing technology to avoid any latency due to communication to the cloud and privacy concerns. However, when sharing at the edge, bandwidth, storage, and computational limitations must be considered.

The mobility ecosystem is rapidly evolving, with the rise of new stakeholders and services, accompanied by new ways for the generation, collection, and storing of data. In the nuMIDAS (New Mobility Data and Solutions Toolkit) project, we provided insights into what methodological tools, databases, and models are required, and how existing ones need to be adapted with new data. We started from insights obtained through (market) research and stake-holders, as well as quantitative modelling. A wider applicability of the project’s results across the whole EU was guaranteed as all the research was validated within a selection of case studies in pilot cities, with varying characteristics, thereby giving more credibility to these results. Finally, through an iterative approach, nuMIDAS created a tangible and readily available toolkit that can be deployed elsewhere, including a set of transferability guidelines, thus thereby contributing to the further adoption and exploitation of the project’s results.

The Flemish government launched two related projects in Belgium between 2020 and 2023. First, they rolled out an Intelligent Truck Parking Service (ITPS) along a part of the E17 motorway. Then we supported them to develop a vision on truck parking. The ITPS consisted of an app for truck drivers, combined with information on variable message signs. We evaluated the ITPS so as to provide answers to the following research questions: (i) which technology best measures parking occupancy? (ii) what is the impact of providing parking occupancy information to the truck driver? (iii) how do users deal with the information (user experience)? In order to evaluate the ITPS, we performed a technical analysis, an im-pact analysis, a user acceptance analysis, and performed interviews with stakeholders regarding the eco-system. In addition, the analysis of GPS measurements provided insights into used parking locations and occupancies for developing the vision.

Guaranteed access to mobility for all is one of the conditions for the participation of all individuals in an inclusive society. On the one hand, there is a significant increase in the availability of multimodal mobility services for people. On the other hand, the increase in the movement of people using different mobility services makes traffic management an even more difficult challenge to overcome. Traffic management must move from the traditional management of traffic volumes to managing the different types of available vehicles. For this purpose, the EU-funded TANGENT project is developing new complementary services for optimising multimodal traffic operations. Data from heterogeneous data sources must be appropriately shared between the stakeholders involved in the project to enable the development and testing of these innovative services. This paper presents the methodology adopted by the TANGENT project for formalising the TANGENT Data Sharing Governance model and adequately managing and facilitating data-sharing tasks.

The advancement of Intelligent Asset Management Systems (IAMS) in the railway sector can be fostered by integrating data and trustworthy artificial/human intelligence. However, the implementation of solutions for maintenance prescription and optimized intervention plans requires the integration of multiple digital artifacts and the involvement of different stakeholders. This paper discusses an IAMS Support Integration Framework, facilitating the integration of diverse digital artifacts for the implementation of intelligent maintenance scenarios in a multi-stakeholder environment. To support the integration, the framework offers functionalities for enhancing data sharing and guaranteeing process tracking within an IAMS. The paper outlines the framework’s requirements and architecture, demonstrates its implementation in practical scenarios from the DAYDREAMS project and presents the preliminary evaluation performed with relevant stakeholders.

This paper aims to present the basic elements of the Smart Contract Framework (SCF) and the value of data sharing in a multimodal system. SCF is a business process that defines data exchange rules among Transport Service Providers (TSPs) that share the common goal of getting the passenger to his/her destination through a multimodal trip chain. It provides a centralized hub for the generation and management of contracts (i.e., data sharing agreements and smart contracts) via a web platform that allows TSPs to create, negotiate and continuously monitoring and making use of signed contracts. Most importantly, the storage of TSPs’ data and all the data sharing processes are executed outside this platform. Moreover, the SCF will provide a data driven environment where exchanged data can be further used for other purposes (e.g., analytics and business relationships’ analysis). Additionally, data sharing has the potential to create benefits for TSPs in the form of greater transparency, reduced costs, increased revenue, strengthened business relationships, etc.

The overreaching goal of this paper is to present how KEYSTONE - a project funded under the Horizon Europe program – aims to develop a knowledgeable, comprehensive, and fully integrated smart solution for resilient, sustainable, and optimized transport operations. Treasuring previous projects that have worked on standardization and learning from the challenges and strength of current European platforms, KEYSTONE has the goal to support the development of a sustainable, efficient, and safe transport system, allowing enforcement authorities to access data for the purpose of checking compliance with rules applied in the transport of goods and passengers. The aim is to tailor standardized digital solutions that can be used from several realities to standardize the transport system. In this paper, the authors present the on-going activities and the methodology that will be followed in order to develop new solutions and demonstrate their validity through an app and through its test with two highly diverse pilots.

What would the advent of Multimodal Traffic Management (MTM) be like in Europe by 2050? This paper provides some answers by suggesting three contrasted sociocultural scenarios that refer to different key societal values.Traffic management has been siloed so far with few or no communication between stakeholders (i.e. network and traffic managers, transport service providers, fleet operators, users, etc.). Yet better coordination based on Data sharing among traffic stakeholders could facilitate the future of more efficient transport from an economic and environmental point of view in line with the European policies. It would also make it easier to manage recurrent or exceptional disruptions.Beyond the technological issues raised up by the sharing of traffic data, this paper’s main challenge is to consider the future societal contexts and values on which European society could be based in the next decades.Based on a literature review, on practitioners’ and experts’ workshops and interviews, the paper provides three explorative scenarios addressing two questions: How could/should data sharing and its governance be implemented? What societal aspects and concerns should be considered when sharing data? These explorative scenarios are thus designed to stimulate debate.

Over the coming years, renewing traffic will, above all, be about making much better use of all kinds of traffic-related data. Data must flow smoothly and be compatible & interoperable between vehicles, different modes of transport, service providers and end users in order for us to create the strongest possible foundation for a sustainable transport system and new transport services and solutions.In a data ecosystem, data is exchanged between platforms using interfaces (APIs). In the traffic data ecosystem, we focus on traffic-related data, such as public transport schedules, traffic disruptions or weather conditions, with which service providers and application developers work together to create better customer experience and services.Those operating in the data ecosystem have a need for rules, as operators have a need to obtain data from each other. The fair data economy rulebook enables operators to better retain control over the data they share and decide on the related access rights and other business rules. The Rulebook is a tool for sharing increasing quantities of data either free of charge or for a fee, as agreed jointly. In practical terms, this will improve confidence in data integrity, quality, security, identity, operator roles and terms and conditions of use.

This project aims to develop an enhanced Intelligent Transport System (ITS) platform at Transport for Greater Manchester (TfGM) to optimise traffic management and improve efficiency. The focus is on leveraging the collaborative capabilities of the Urban Traffic Management Control (UTMC) system for strategic management of the road network through digital data and smart mobility assets, enabling effective traffic management and fostering collaboration among stakeholders.The delivery of the ITS platform will enable the ability to make informed decisions, efficient traffic management, and a holistic approach to transportation. By leveraging the collaborative capabilities of the UTMC system and digitising legacy systems, the project paves the way for a resilient and future-ready transport ecosystem.

Combined Transport (CT) is one of the most sustainable and safest mode of freight transport in Europe using the advantages of road for the first and last mile and rail for the long-haul journey. The CT chain consists of a comparatively large set of actors and organisation specific rules. Operational issues like a low level of punctuality and related company-specific delay management block efficient quality management and concerted improvement across multiple stakeholders. Under the CEF programme, the European Climate, Infrastructure and Environment Executive Agency (CINEA) co-funded the EDICT project to find pragmatic solutions to ease the connectivity of several existing platforms and to develop standardised master and transaction data to provide a set of interoperable platforms that facilitate the data sharing and interaction between key CT stakeholders. The core innovation is a collaborative Quality Management System (cQMS) that is conceptualised and implemented based on harmonised and improved existing company specific standards for timestamps, delay and cancellation reason codes, and master data. The project partners intend to share a common process design and use a set of interoperable software-as-a-service solutions to improve process and cost efficiency. During the two-year EDICT project lifetime, eight CT stakeholders, under the coordination of the industry association UIRR and consulting partner Consilis, jointly conceptualise, implement, and run a 5-month cQMS demonstrator pilot based on actual operational data.The project aims to facilitate the digital integration of Road-Rail Terminal Operators, CT Operators, Railway Undertakings and Infrastructure Managers to provide accurate information and ultimately to improve the overall punctuality, reporting and data analysis capabilities, and service quality to door-to-door customers (LSPs and shippers). Thereby, the project contributes to achieve the EU’s policy objectives to increase the share of rail, to push forward the greening of freight transport and contribute to the European mobility data space. The targeted project results lay the foundation for an industry ecosystem solution providing standardised electronic data interchange and sector-wide processes to improve the service quality of regular Combined Transport trains.Technically, several digital platforms are either introduced, modernised or integrated to achieve the best cost-performance ratio and become attractive for a larger group of customers. Piloting 5 regular trains on 3 different TEN-T corridors will serve to learn in practice about benefits and improvement potentials. The evaluation will be used to refine the business model and the adoption strategy to facilitate a future roll-out to become an industry standard. Consequently, not only the CTOs but also the shippers and LSPs will profit from better information transparency on the status of their goods, causes of delays and long-term improvements based on the analysis and elimination of the root causes of disruptions. Thereby, the project and its future roll-out will significantly contribute to a higher attractiveness of Combined Transport and to achieve the declared objectives of the Green Deal, Fit-for-55 and REPowerEU policy initiatives.

Tourism destinations are often confronted with the challenge of balancing the capacity of their transport systems, between peak and off-peak periods. In order to properly design, implement and measure solutions’ impacts, data on mobility demand are required. This paper presents an integrated mobility data monitoring scheme, tested for the first time in Rethymno and Platanias, two small insular cities in Crete. The monitoring scheme included mobility and environmental indicators measurements, field research and data analysis methods. Results helped to capture the variation in traffic flows and driving behaviours by vehicle type and daytime, between winter and summer, and better understand the city’s seasonal mobility characteristics. Cities of similar typology (Mediterranean climate, seasonal flows, linear coastal development, limited transport network capacity) can benefit from such simplified schemes. Since the cost is low, cities can acquire real-time data to feed smart tools, like GIS models. Data interpretation allows policymakers, governmental authorities and tourism stakeholders to design traffic calming measures in high-risk locations, to enhance traffic safety for citizens and visitors and improve conditions for active mobility, as well as to introduce policy measures and behaviour change activities for drivers. Cross-analysis with tourism and user behaviour data supports overall mobility and resilience planning.

The City of Helmond aims to offer new mobility services to users by operating on public roads a fleet of remotely supervised automated public transport vehicles from/ to mobility hubs/stations to Helmond’s neighbourhoods and (economic) hotspots. The first step has been to set up two pilots with automated shuttles in 2021 and 2022 (in FABULOS and LivingLAPT EU projects). These two pilots showed that some improvements of the automated shuttle service are necessary to offer a quality of service (speed, comfort, perception of (social) safety) at least equivalent to the current conventional public transport.To go beyond these projects and to achieve Helmond’s ambition, Helmond participates in specific projects (like Move2CCAM – on citizen/organization attitudes and on the impact of CCAM) and adopted a “Roadmap for automated public transport services in Helmond” in 2023. A stepwise approach has been chosen to develop and test automated public transport services in order to be able to implement and upscale them in the city. Starting with automated public transport service operations in an easy environment and then later in more complex environments. In 2024, we will start with the first step; an automated public transport service without the need of a (remote) safety driver in “Bedrijventerrein Zuid Oost Brabant” (BZOB) – an industrial area with an easy Operational Design Domain (ODD).

This paper describes the development of an optimization model that considers the travel times of buses and traffic patterns to minimize deviations from planned schedules for routes across a network in a European city. Traffic congestion is a significant challenge which negatively affects the on-time performance and reliability of bus services. To address this issue, smart traffic management systems such as SCATS (Sydney Coordinated Adaptive Traffic System) are implemented to enhance traffic flow. While SCATS allows buses to request priority at road junctions controlled by traffic signals, conflicts may arise when different buses have opposing requests. To address this, we use a stochastic optimization approach to determine an optimal time-specific traffic interference solution that network owners can impose, such as signal phasing, to minimize schedule deviations for buses throughout the entire network.

It is an essential requisite in order to achieve safe autonomous driving in urban environments, to address the mobility from a multidisciplinary approach. In this sense, technological development is crucial in both passenger and goods transport. Within Integra project, in which the work described here is framed, autonomous and connected driving is addressed from a dual perspective: urban environments and logistics environments. Thus, its activity is structured around the following lines of action: (1) improvement of perception technologies, (2) optimisation of communication technologies, (3) redesign of occupant damage mitigation technologies and (4) development of new delivery solutions. This paper summarizes the most critical aspects of each of these lines and the considerations to be taken into account to ensure the implementation of autonomous and connected driving in complex environments. In doing so, it takes into account the particular conditions of urban environments and their implication for the deployment of autonomous mobility.

Most sustainable urban transport strategies include an expanded role for public transport amongst their measures. New technologies have often been presented as a means of making public transport more attractive when compared to other travel options. Integrating attractive transit with new development is also presented as a technique which can encourage public transport use. The present paper considers these three principles in the context of the MultiCAV project, which tested automated buses (AB) in Didcot (UK), a town scheduled for significant expansion. MultiCAV is one of the few automation projects to test urban buses capable of providing mass transit in a range of urban and peri-urban road environments. Findings are presented from evaluative research including surveys and interviews with potential users, actual users, safety operatives, and the project delivery team. It is found in the case of MultiCAV that automation technology has a high degree of capability, but is not yet sufficiently mature to operate a schedule service in complex road environments shared with general traffic without human assistance for specific tasks. Additionally, a solution for the tasks currently performed by drivers beyond the driving task also needs to be identified. Passenger acceptance of automation was found to be strongly influenced by the condition that an operative is onboard. If early applications of ABs were to be on segregated busways this would reduce or remove some of these barriers. Segregation would also give the services priority, capitalising on the cultural value of high-technology public transport, and increasing appeal relative to the private car. Such an approach would be particularly relevant for new development areas where segregation would be relatively easy to achieve.

This paper studies the collaborative and managerial aspects of remote control of trains and trams. In the transport industry, increasing societal pressures have pushed organizations to revolutionize their operations by going digital. Indeed, contemporary societies require that transport firms optimize their services in order to meet the growing challenges and needs of the modern world. Our study is based on mapping published experiences and expected consequences of remote train control (RTC). The identified initiatives yielded valuable insights into the potential challenges associated with RTC and we provided best practices to address obstacles and navigate the changes in the work context for various stakeholders. We found that misunderstandings in the driver´s roles can cause quality of services issues during the transition to RTC. The prospect of changing roles for operational staff raises concerns. The achievement RTC will be contingent upon the involvement and collaboration of diverse stakeholders.

We present the Qualitative Explainable Graph (QXG): a unified symbolic and qualitative representation for scene understanding in urban mobility. QXG enables interpreting an automated vehicle’s environment using sensor data and machine learning models. It leverages spatio-temporal graphs and qualitative constraints to extract scene semantics from raw sensor inputs, such as LiDAR and camera data, offering an intelligible scene model. QXG can be incrementally constructed in real-time, making it a versatile tool for in-vehicle explanations and real-time decision-making across various sensor types. Our research showcases the transformative potential of QXG, particularly in the context of automated driving, where it elucidates decision rationales by linking the graph with vehicle actions. These explanations serve diverse purposes, from informing passengers and alerting vulnerable road users (VRUs) to enabling post-analysis of prior behaviours.

The continuous development of advanced driver assistance systems is paving the way for the deployment of autonomous vehicles. Until then, manual vehicles and partially automated vehicles (AVs) will co-exist. Past experience has shown that the deployment of new technology must consider users’ acceptability and adoption. This is the case of AVs, that must provide safety and comfortable travel experiences. To evaluate acceptability profiles towards AVs, this study analyses the determinant factors of AVs acceptability to identify different Portuguese population clusters. A questionnaire was developed to explore prospective users’ representations regarding benefits and expectations, risks and concerns, previous experience with automated driving technology, and preferred use cases for AVs. A cluster analysis was performed using the k-means algorithm and, after, chi-square tests characterized cluster membership. In the end, acceptability profiles were compared for different use cases using ANOVA post-hoc tests. Three clusters of prospective users were identified: objectors, ambivalents, and enthusiasts. Driving pleasure, safety, reliability of the technology, and data privacy are prevailing negative factors while improved road safety, reduced emissions, and non-driving tasks possibilities favor acceptability. Sociodemographic characteristics, like income, education, place of residence, and self-perception about the adoption of new technologies reflected the main differences between clusters.

Automated road transport mobility will develop only if fundamental conditions are fulfilled: acceptance by users and citizens, economic sustainability, contribution to a more sustainable mobility and last but not least, demonstration of its safety. Fulfillment of these conditions needs to be addressed by policymakers through regulations, standards, guidance, assessments and stakeholders’ involvement. France built its regulatory framework on this balance by assuming safety will be the main factor for other conditions for the development of automated road transport systems to be reached. This paper presents the safety demonstration framework, methods and tools for the development of automated road mobility. The main guiding principle underlying this safety-first based framework lies in the scenario-based approach aiming at the best possible coverage of driving situations that such systems should be required to encounter and address safely. This paper will present two aspects of this approach, articulated with conventional safety demonstration activities: scenario generation approach based on layers and its articulation with ODD description and OED definition.

The goal of ULTIMO Horizon Europe project, is to create the very first economically feasible and sustainable integration of AVs for Mobility as a Service (MaaS) public transportation and Logistics as a Service (LaaS) urban goods transportation. To do this, a user centric holistic approach is adopted, to ensure that all elements in a cross-sector business environment are incorporated to deliver large-scale on-demand, well-accepted, shared, seamless-integrated and economically viable Connected and Cooperative Automated Mobility (CCAM) services. In this concept, this paper presents the work performed to bridge the needs of the users with the vehicles’ potential. To do this, extensive research has been performed in identifying the requirements of all involved actors, including existing research findings, conduction of interviews, co-creation workshops and round table discussions, along with market survey for specifying the existing vehicle capabilities and experts’ consultation for matching the requirements with the capabilities. This provides a tool to pilot sites for identifying the requirements their vehicles satisfy or choosing the appropriate vehicles according to the requirements existing (or planned) to be treated at site. Moreover, it could serve vehicle manufacturers for developing further capabilities for their vehicles, as well as planners and PTO’s in developing their strategies.

The development of Cooperative, Connected and Automated Mobility (CCAM) represents major challenges for local authorities, particularly in less dense and rural areas where there is a lack of public transportation. The main purpose of this paper is to describe the development of an automated mobility on-demand service, which will be implemented beginning September 2023 in La Rochelle, France. This paper describes the automated mobility on-demand system (bus, infrastructure and supervision centre) and the conditions for its smooth integration with the local existing MaaS (Mobility as a Service) system. This new service aims at providing a small/medium sized city like La Rochelle with an efficient last-mile transport service - to and from existing mobility hubs - complementing the existing public transport.

While dynamic ridesharing has been extensively studied, there remains a significant research gap in exploring role flexibility within the many-to-many ridesharing scheme, where the system allows for several pickups for drivers and multiple transfers for riders. Previous works have predominantly assumed that all participants own a car and have focused on one-to-one arrangements. Additionally, there is a scarcity of research on integrating High Occupancy Vehicle (HOV) lanes and mathematical modelling. This study addresses these gaps by presenting a novel Mixed Integer Linear Programming (MILP) model that allows for role flexibility irrespective of car ownership and considers the implications of HOV lanes. Computational analysis highlights the benefits of incorporating role flexibility and accommodating non-car-owning participants in many-to-many ridesharing systems. Yet, excessive role shifts may create imbalances, impacting service to non-car owners. Further research should explore these correlations.

Throughout the history of transportation, very few inventions have had the same impact as rail transport. One of the oldest and most established means of transportation, railways, still provides efficient transportation of freight and passengers, but they stand to benefit from cutting-edge technology. The Europe’s Rail FP2 R2DATO project (GA 101102001) is developing technologies in several fields of digital automated up to autonomous train operation seeking a new paradigm in how the rail system is operated, increasing safety, flexibility, capacity, performance and reducing energy consumption and costs.These technologies can be applied to all rail segments including urban light rail. Future trams will benefit from new functionalities including remote control, ATO or perception systems. After collecting operational use cases and operational rules from key players of the urban operating community in Europe, FP2 R2DATO activities on tramway will focus on the remote control operation first, which will allow an operator to drive the vehicle from the office, avoiding the need of getting physically to the tram. This feature is intended to be used in daily operation in depots to support the train preparation, its retirement from service or for shunting within the depot. A first demonstrator is planned in 2024.

The paper focuses on the behaviour of cooperative, connected and automated vehicles (CCAVs) with the aim of improving traffic flow and safety and providing adequate comfort to vehicle occupants. The study is part of AI@Edge project, founded by the Horizon Europe framework programme. AI@Edge focuses on leveraging AI and Edge computing to enhance 5G networks. The simulation environment is a single-lane mini-roundabout, calibrated on the basis of experimental measurements to accurately replicate the behaviour of human-driven vehicles. A cooperative Deep Reinforcement Learning (DRL) policy, exploiting Proximal Policy Optimization (PPO), was developed to optimize the behaviour of CCAVs while negotiating the roundabout. To assess the effectiveness of this policy, a dynamic driving simulator coupled with a microscopic traffic simulator and a graphical simulator was employed. This comprehensive approach included both simulated human-driven vehicles (HDs) and CCAVs, alongside a real human driver. Tests indicate that human drivers respond positively to scenarios with a higher percentage of automated vehicles, due to an enhanced sense of safety and comfort. Quantitative analysis of the policy also demonstrates the capability of CCAVs to reduce fuel consumption and optimize traffic flow.

Railways currently use the Global System Mobile for Railways (GSM-R) for train operation across the world. The GSM-R is used for voice communication between train driver and control centres, including but not limited to, the Railway Emergency Call (REC) as well as for signalling and control messages of the European Train Control System (ETCS). Based on the fifth generation (5G) of mobile networks and in particular, 3rd Generation Partnership Project (3GPP) releases 17 and 18, the Future Railway Mobile Communication System (FRMCS) is envisioned to replace GSM-R. This modernization step does not only address administrative constraints, e.g., the upcoming 2G equipment obsolescence, but also provides an opportunity for transforming the current train operation through introduction of digitalization and automation technologies in an aim of increasing capacity and efficiency for a sustainable transport. In this paper, we present some highlights of first FRMCS Fields trials that have been realized within the 5GRAIL project which is funded by the EU Horizon 2020 program. These trials were co-led by DB Netz AG in Germany and SNCF RESEAU in France. The aim was to accomplish functional and performance testing in addition to multi-access and cross-border like scenarios. We discuss some results on the testing of the different applications before concluding with lessons-learned and outcomes.

Future Railway Mobile Communication System (FRMCS) will be the 5G SA MCX standard for railway operational communications, conforming to European regulation. It will also fulfil the needs of rail organisations worldwide. UIC (International Union of Railways) is leading the design, standardisation, and is deeply involved for the introduction plan of this telecommunication system, in close cooperation with railways, authorities and suppliers. FRMCS is the successor of GSM-R which is a component of European Railway Traffic Management System (ERTMS), representing around ~130,000 km of coverage of tracks in Europe. GSM-R is however announced to become obsolete soon due to its 2G technology. Therefore, FRMCS, being specified and implemented as a standard, combining 5G transport with Mission Critical (MC) service layer features, is considered as a major trigger for the wide-ranging digitalization of the rail sector, satisfying the increasing demand of data while keeping the high quality of service for critical railways applications, in an interoperability context.This paper aims to present some insights of the FRMCS introduction plan and provide an overview of the outcomes of 5GRAIL, an EU Horizon 2020 project coordinated by UIC, as part of the FRMCS readiness initiative.For more information, visit: https://uic.org/rail-system/telecoms-signalling/article/frmcs , 5GRAIL - 5G for future RAILway mobile communication system.

The Future Railway Mobile Communication systems (FRMCS) is the new system under development, based on 5G-NR, that will replace the GSM-R standard. Its deployment will require the design of new radio engineering tools, which in particular rely on new 3D radio channel models to validate the performance of proposed networking technologies under high-speed conditions, and to perform future radio-planning tasks. The 5GREMORA project aims at developing new measurement-based 3D radio channel models for railways, and demonstrating how new scheduling algorithms can be assessed with those models. The paper outlines the main ambitions of the project; and presents the preliminary results obtained so far.

High-accuracy GNSS (Global Navigation Satellite System) positioning requires the receiver to use correction data. This data is typically delivered via 4G mobile internet. In this paper, we present a novel method to deliver the data via C-ITS (Cooperative Intelligent Transport Systems and Service). We compare its performance against using 4G and analyze the impact on the accuracy during data gaps, all using data collected in test drives from a real deployment in a small segment of a motorway. The results show that with C-ITS, a comparable performance can be achieved. The observed 2D position errors in our tests were below 3.1 cm for 95% and below 10 cm for more than 99% of the time.

This study investigates the potential integration of eCalls, mandated by the eCall Legislation for vehicles from March 31, 2018, into road operator Incident Management Systems (IMS). We utilize eCall data from the “SRTI Ecosystem” and match it with IMS incidents to verify the eCall data and enhance the IMS with the most accurate available timestamp for accident occurrence. To assess the potential temporal gains of eCall integration, we introduce a metric to quantify the time saved by incorporating eCalls into event management systems. The proposed metric was evaluated over a three-month period in 2023, and the results indicate that integrating eCalls into IMS is a viable step to expedite the incident management process.

This paper analyzes the impact of Accessibility based on GPS data for both motorized and non-motorized modes on distance traveled by private and public transport users in developing countries. The data was collected from a mobile phone app called Inertia. A neural network model was developed for the dependent variable distance. The independent variables are the Level of Service (time, speed) measured via GPS data, public transport (e.g., distance to the nearest station, number of stops/stations within the catchment area) called PT Availability, and accessibility (off-peak and peak neighborhood and station accessibility). The results show that the type of station and neighborhood peak access promotes better modeling of the distance.

This paper presents a comprehensive market research study focused on identifying opportunities and barriers for the adoption of autonomous heavy-duty vehicles (AHDVs) in the logistics industry. The methodology employs a qualitative approach, utilizing workshops, questionnaires, interviews, and social media polls to collect primary and secondary data. The empirical findings highlight both the current landscape and the future trends in AHDVs, fleet management, and logistics. The study reveals the importance of safety, technological readiness, regulatory challenges, and infrastructure development as key barriers, while also showcasing potential benefits like increased safety, reduced costs, and improved efficiency. Furthermore, a quadrant analysis prioritizes these opportunities and barriers for strategic decision-making. The article concludes by discussing the implications of the findings and the broader context of autonomous vehicles in logistics.

As urbanization continues to increase worldwide, cities face the challenge of accommodating growing populations while maintaining efficient and sustainable transportation systems. The advent of connected and autonomous vehicles promises transformative changes in urban mobility. This paper addresses developments and innovations aimed at seamlessly integrating CAVs into the complex urban mobility ecosystem. It presents assumptions related to a fleet of fully connected and autonomous vehicles coordinated by traffic management centers and focuses on optimizing route assignments based on various performance metrics, including travel time, energy consumption, congestion, and emissions. We are also exploring the integration of people and goods mobility by leveraging the cost efficiency and versatility of on-demand autonomous services.

In recent years, reducing emissions in Freight-Transport has become a top priority in mobility. This paper presents the methodology for the full control loop of a truck’s powertrain, which has been transformed from a polluting GLP powered ICE to a zero-emission Hydrogen-powered electric powertrain within the SHINE-Fleet project. The adaptation has provided the ECU with increased flexibility to control the dynamics, enabling automated-driven missions like docking. The paper covers infrastructure-based localization for the vehicle, path planning to reach the setpoint, and control implementation with a Model Predictive Controller (MPC).

In this work, a control architecture is proposed for controlling a convoy of automated vehicles that are mechanically connected to each other. Connection allows them to circulate without specific permissions in any environment, even if a driver is present only in the leading vehicle. The connection in the real case is an elastic and compliant connection, a condition mediating the cases of rigid connection and absence of mechanical connection. To evaluate the capabilities of automation control algorithms in maintaining a prescribed path and estimate the required stiffness of the mechanical connection, simulations have been performed considering a geometric control algorithm (Pure Pursuit controller) for lateral control mixed with a PID controller for longitudinal control. Considering several trajectories of two convoyed vehicles, the control method has been analyzed based on the minimum value of errors (longitudinal and lateral) in vehicle trajectories. The ideal case is considered where all communication and environment scanning systems operate with maximum efficiency. While subsequent steps will be made to further decrease the trajectory error by modifying the vehicle control, the results enable an estimation of the required compliance of the mechanical connection expressed in terms of trajectory error.

The work described in this paper provides valuable insight into the novel PDI solutions to be developed for CCAM support within the Horizon Europe-funded AUGMENTED CCAM project. To this aim, two different test sites are being prepared in Madrid, Spain, for the implementation and demonstration of four cutting-edge solutions: Equipped Vulnerable Road Users (VRU) protection; Traffic Management Optimization based on Probe Vehicle Data (PVD) from CCAM; Emergency Vehicle approaching; and Ad-hoc on-demand unmanned aerial vehicle (UAV) based VRU protection for closed environments. This document unveils how the proposed services underline the vast potential of CCAM when synergized with advanced Infrastructure support, which is not only limited to the extension of the Operational Design Domain of Connected and Automated Vehicles. This approach also demonstrates PDI’s capacity to significantly enhance road safety, traffic efficiency and sustainability. Additionally, the importance of the Digital Twin is highlighted as an indispensable element for advanced traffic management and comprehensive infrastructure monitoring.

The sensors and cameras in a car are limited to the information that the vehicle receives and processes in its immediate environment and line-of-sight applications. To further extend these data collection capabilities (with the intention of increasing traffic efficiency and safety), Cooperative Intelligent Transportation Systems (C-ITS) technologies are based on the exchange of information between vehicles through wireless communication systems and networks. This paper shows how IDIADA addresses this reality and proposes a pioneering and innovative solution for wireless networks. IDIADA has built a Connected Vehicle Hub, equipped with the latest mobile communication technologies and in compliance with the ETSI ITS G5 standard, to facilitate the construction of increasingly reliable systems, offering its facilities and adapting the platform to the needs of specific test scenarios, in order to repeat tests and evaluations that are difficult or even impossible to perform on the public road due to the constant changes in the road environment and in the networks themselves.

The working group Road Freight Transport in CEDR (Conference of European Directors of Roads) performs different tasks. One is about Intelligent Access, later only referred to as IA. The used definition of IA is to ensure “the right vehicle on the right road at the right time with the right weight”.The goal for the work is to collect best practice of IA and recommendations for implementation. IA is a rather new concept in Europe but has been used in Australia for more than 15 years (TCA 2018).Conclusions from the work so far are. A survey and in-depth interviews show that National Road Authorities sees the concept of IA as something with many possibilities. Using IA as an enforcement tool is the most obvious interpretation of the concept and when scaling up also other opportunities become visible, such as better coordination of traffic and logistics, better use of infrastructure, control of emission zones, monitoring of abnormal transport and transport of dangerous goods.This gives possibilities for almost all stakeholders and therefore also for society. In this way, can also NRAs improve the quality of their services. The example from Italy, there you use IA for abnormal transports, shows a use case that give benefits to all involved and showing benefits and/or incentives for all involved will be important for the implementation of IA.

The proposed AI Act lays down requirements for so-called high-risk AI-systems that need to be met by the high-risk AI-system and its provider. These requirements contain core elements for trustworthy AI such as transparency, technical robustness, and human oversight. Automated vehicles are considered to be high-risk AI-systems, but nevertheless these requirements do not (fully and directly) apply to these vehicles. Automated vehicles are excluded from the scope of the proposed AI Act, as the functioning, safety and security of cars driving on EU public roads are governed by existing acts specifically applicable to vehicles. This causes a gap between the findings of two ethics expert groups (EC High-Level Expert Group on AI and the Horizon 2020 Commission Expert Group to advice on specific ethical issues raised by driverless mobility) and the EU legislation for AI-systems of automated vehicles. This contribution will therefore explore two questions: what current legislation is in place that contributes to achieving the core elements or principles of trustworthy AI in automated vehicles and how can a (future) legislative framework for automated vehicles realize these core elements for trustworthy AI?

The gradual introduction of automated vehicles (AVs) rises a variety of challenges. Besides technical issues, legal aspects need to be considered, as nowadays driving regulations are orientated towards human drivers. Policy makers will have to define a legal framework for AVs, which will also effect an AVs’ driving behaviour and therefore also impacts traffic flow characteristics. To investigate possible legal adjustments for AVs and their consequences, traffic flow simulation (TFS) is an established tool. However, applying TFS requires much simulation and modelling expertise. Hence, we present a newly developed simulation platform, which allows to intuitively define, simulate and analyse a simulation scenario for different generic multi-lane highway segments without extensive TFS expertise. In addition, five exemplary simulation scenarios are investigated, focusing on the effect of temporarily increasing the desired time headway of AVs on the main road in order to assist merging vehicles at the on-ramp. The results showed rather negative effects, as congestion was increased in space and time due to temporarily increasing the time headway of AVs.

Current Automated Driving Systems (ADS) immaturity causes a lot of uncertainty for road authorities as they cannot decide with confidence what is the best way to anticipate ADS development and deployment to preserve operational safety and efficiency on their road network. Typically, the actual competencies of ADS in the operating environment are not entirely known and ADS capabilities are regularly overestimated or underestimated based on assumptions that are derived from the scarce information that is publicly available. At the same time, many different situations can occur on open roads and in variable traffic and weather conditions, in particular when these roads are dynamically managed by the road operator (e.g. lane, speed and tunnel management). It is natural that National Road Authorities (NRAs) are concerned about the introduction of ADS that execute the complete dynamic driving task. The most constructive and perhaps only way forward is to create a dialogue between road authorities, automation system developers and regulators. The decision making process presented in this paper aims to support NRAs in this conversation.

Building on the insights provided by the EU CCAM Partnership SRIA, this paper introduces a new double-funnel methodology for eliciting stakeholders’ and users’ needs and requirements, developed and tested within the CONDUCTOR EU research project (GA 101077049). This methodology aligns with the EU's holistic approach by integrating formal inputs from institutional stakeholders with specific, service-oriented inputs from users and other stakeholders, such as industry and service operators. The approach relies on three components. A top-down analysis maps mobility policy principles and outlines social and regulatory requirements for design, encompassing safety, environmental protection, inclusion, accessibility and social well-being. Complementary, a bottom-up perspective captures user and stakeholder needs related to specific use cases. The integration of these results maps CCAM-specific needs into the broader social and regulatory framework. Applied to three use cases in the CONDUCTOR project, this approach contributes to methodological research by testing an integrated, user-centred framework for designing and evaluating CCAM solutions.

The safe deployment of Connected, Cooperative and Automated Mobility (CCAM) systems needs to take into account advanced human-machine interaction. In fact, CCAM is going to change both the cooperation between the vehicle and the human driver and the interaction with road users. This paper presents DriSMi, an advanced cable-driven dynamic driving simulator that enables safe, affordable and reliable CCAM testing. DriSMi can be used with the objectives of (1) modelling and testing the technology and (2) characterizing and modelling the driver, in particular analysing and modelling the driver’s behaviour, ergonomics and safety in different infrastructure/weather/traffic scenarios. After describing the features of DriSMi, a CCAM scenario and a procedure for assessing the acceptability of Adaptive Cruise Control are presented.

For automated logistic transport, cross border issues need to be solved, including technological, organizational, and regulatorily challenges. In this paper we will specifically investigate communication continuity across the border between Sweden and Norway as a use case within the EU financed MODI project, where cross border logistics will be demonstrated driving from Sweden to Norway. The current development within automated vehicles suggests that connectivity will play a crucial role for enabling such solution, including options such as remote surveillance, video stream, downloading map and traffic regulations, retrieving correction data for accurate positioning etc. Driving cross borders poses a challenge in this regard, because roaming to a new mobile network must be performed and typically results in loss of service. In this paper we investigate the expected delays and service behavior when crossing the border and relate the results to the need of the future automated vehicles.

The SINFONICA project aims to develop innovative strategies, methods, and tools to engage CCAM users, providers, and other stakeholders to collect, understand and structure their needs and concerns. This is only possible with the constant involvement of citizens, future users, stakeholders, and service providers linked to CCAM. But how can we be sure that every category is correctly represented? That every voice, every thought, every opinion is expressed and heard? This paper presents a methodology for defining the research groups and so-called groups of interest which is based not just on the analyses of the literature and projects, but also on the involvement of four municipalities across Europe which offer an immersed view within their reality. With numerous geographical, structural, and cultural differences, these municipalities are involved in the co-definition of the research groups and groups of interest so that each group can give voice to all the ones that need to be represented. The results presented in this paper summarize the process of the co-creation of the research groups and groups of interest, their final composition as well as the participatory approaches that will be used with the different categories.

This paper summarizes work from a BRRC working group with external experts, which examines the contribution of infrastructure to road traffic safety, regarding connected and autonomous vehicles.We gained insight into relevant traffic safety matters linked to the road infrastructure component. The working group addressed questions such as: are advanced vehicles safer than traditional vehicles? How do new risks relate to potential road safety gains? What relevant background information is available? What about the role of road infrastructure, traffic signs and road markings? How important are digital twins?Based on literature review and expert discussions, relevant knowledge about road infrastructure issues in relation to the safe authorization of autonomous vehicles was collected and summarized in a BRRC publication for road owners and other partners in the road construction sector. The most important conclusions are presented in this article.

The objective of NAITEC URBAN LAB is the creation of a reference environment that allows the design, testing, and deployment of the different technologies and functionalities associated with the connected, cooperative, and autonomous vehicle (CCAM). The urban test bed is in Pamplona, Navarre, Spain. It is a circuit in the heart of the city, next to a university campus. In addition to being a real environment in the urban area, it presents some very interesting characteristics in terms of mobility scenarios, having a university, a football stadium, and a music hall in its surroundings. The urban test bed has different sensor systems. It has a network of fibre optic sensors; traffic cameras; a LiDAR for the detection of vehicles and pedestrians; and a network of pollution sensors. It also presents a V2X communications equipment, both in infrastructure and in vehicles. The collected traffic data will be used for the creation and validation of traffic models.

The paper presents the concept of Distributed Operational Design Domain (ODD) attribute Value Awareness (DOVA) developed by TM4CAD (Traffic Management for Connected and Automated Driving), a CEDR-funded project involving MAPtm (coordinator), Traficon, Transport & Mobility Leuven, and WMG, University of Warwick, as well as Hironao Kawashima from Japan and Steven Shladover from USA as experts. TM4CAD explored the role of infrastructure systems and connected vehicle data exchange in creating ODD (Operational Design Domain) attribute value awareness for Automated Driving Systems (ADS), so that each ADS can be forewarned before it encounters conditions in which it will not be capable of operating, i.e. ODD departure. TM4CAD proposed various approaches for providing distributed ODD attribute value information and its importance, acquisition principles, and corresponding data quality requirements. It also discussed the basic mechanisms of ODD management via specific use cases, which build on the interaction between traffic management systems and the ADS, and highlighted the need for stronger interactions between national road authorities and ADS developers.

Road traffic congestion has adverse effects on commuter safety and transport network efficiency, apart from its environmental consequences. To address this issue, Traffic Incident Detection (TID) models have been developed, leveraging advanced connectivity technologies. However, ensuring the alignment and effective operation of these technologies within existing systems and contexts is critical. This research aims to create an incident detection algorithm supported by Vehicle-to-Vehicle (V2V) technologies, alerting road users approaching incident zones. The algorithm’s effectiveness was assessed through metrics like vehicle delays, travel time, and macroscopic fundamental diagrams (MFDs). Real-time traffic conditions were simulated using VISSIM, employing data from Inductive Loop Detectors (ILDs) and ground truth data from an instrumented vehicle on a UK motorway section. Results reveal varying impacts on delays and overall traffic based on V2V adoption rates. The presence of Connected Vehicles (CVs) ensures efficient traffic flow. These insights benefit network operators, enabling prompt identification and communication of traffic incidents to drivers, roadside infrastructure, and traffic control centers, ultimately aiming to mitigate traffic and safety impacts.

Motorways in Ireland have experienced growth in demand over the last few decades due to a rise in vehicular traffic, impacting greatly on the operational capacity of the various high mobility conduits during peak hours. This is especially the case in Cork city on the N40, M8, N8 and N25 motorways on their approach to the Jack Lynch Tunnel and Dunkettle Interchange. Whilst this increased activity may be indicative of economic growth, the recurring congestion resulting from this reduces the mobility of road users at peak hours and is likely to impact the environment negatively through increased greenhouse gas (GHG) emissions. The increased vehicular activity is also highly likely to have adverse implications on road safety across the highly trafficked sections of motorway in and around Cork city. To manage the demand and remedy the impacts thereof, there is active deployment of various Intelligent Transport Systems (ITS), in line with Transport Infrastructure Ireland (TII)’s vision “to ensure that Ireland’s national road and light rail infrastructure is safe and resilient, delivering better accessibility and sustainable mobility for people and goods”. This paper seeks to highlight the pragmatic approach selected, including the key considerations and the challenges encountered in the development of the concept and design of the EU-funded MERIDIAN Cork Co-operative Intelligent Transport System (C-ITS) on behalf of TII.

Collective Perception (CP) allows connected autonomous vehicles to share and fuse processed sensor data via V2X communication. It can potentially allow for an increased object update rate, extended field-of-view awareness, and redundancy but it first requires a thorough evaluation and validation. Due to the CP’s field testing practical challenges most of the previous work on CP has considered large scale-simulations with a focus on connectivity/network aspects. More recently, large-scale collaborative perception synthetic datasets and open source benchmarks have appeared, allowing the perception engineers to study CP from a perception point of view, which is missing so far. In this paper, the first building blocks (work in progress) towards CP scenario-based testing for a roundabout navigation use case are been set by proposing a Bayesian CP algorithm and its testing plan. The CP algorithm is described and metrics for CP assessment are discussed focusing on the fused information content produced by the algorithm. The next steps towards a hybrid evaluation plan combining real-world agents and simulation are outlined.

In the context of Connected, Cooperative, and Automated Mobility (CCAM), precise ego-vehicle positioning and environmental status assessment are crucial. However, these tasks can be susceptible to sensor failures, misuse, and cyberattacks. Automation disengagements and system redundancy are common strategies to achieve Minimum Risk Conditions when failures occur. This paper presents a Fail-Safe decision architecture formulated within the framework of the SELFY project ( https://selfy-project.eu/ ). The main aim is to reduce inaccuracies in GNSS-derived positioning through the incorporation of sensor fusion, AI-guided situational assessment, trajectory planning, and mode decision components. Additionally, the architecture has been designed to enable real-time updates and communication with external entities, including the Vehicle Security Operations Centre.

Connected and Cooperative Automated Mobility (CCAM) applications, instrumental in enhancing vehicle communication with infrastructure and the cloud, face cybersecurity vulnerabilities due to their intricate components requiring multifaceted cryptographic validation. With quantum computing advancements, traditional public key cryptography becomes susceptible, emphasizing the need for quantum-resistant algorithms to assure long-term automotive cybersecurity. Although Post-Quantum Cryptography (PQC), which provides solutions to counter this quantum risk is still under research, several algorithms have emerged and are under standardization. This paper provides a comprehensive overview of the status of PQC in automotive applications, including a performance analysis of selected algorithms in this research. Further, it presents some potential use cases of PQC (such as Secure Over-The-Air (SOTA) software update and Vehicle to everything (V2X) communication). By relying on PQC, the automotive industry can stay ahead in securing connected vehicles against emerging quantum computer threats.

SELFY envisions an agnostic toolbox for the self-management of security and resilience of the CCAM (Connected, Cooperative and Automated Mobility) ecosystem, which can be easily deployed to extend the current Operational Design Domain (ODD), providing self-awareness, self-resilience and self-healing mechanisms and enhancing trust between stakeholders. SELFY is based on four pillars: Situational awareness, Resilience, Secure Data Sharing and Trust and provides three groups of tools. SACP (Situational Awareness and Collaborative Perception) tools aim at providing all CCAM actors with a comprehensive understanding of their environment, i.e., the perception of objects, such as other traffic participants and stationary objects. CRHS (Cooperative Resilience and Healing System) tools enable self-protection actions whenever a compromising situation is detected in relation to assets, vehicles, operations, or the system itself. TDMS (Trust and Data Management System) tools establish a secure and trusted environment for data in a collaborative and cooperative context, both for infrastructure and assets, as well as for citizen’s data, such as drivers or pedestrians with special attention to privacy considerations. By defining a collaborative environment between the different tools to respond to new threats, risks and attacks SELFY facilitates the comprehension of new challenges in the cybersecurity aspect of CCAMs.

Growing traffic volumes in cities demand new ways to manage routes. Current Intelligent Transport Systems (ITS) face safety and privacy issues, high data costs, and security vulnerabilities. We propose a lightweight system using fog computing and homomorphic encryption. Vehicles anonymously share encrypted travel data with a trusted fog node, allowing the Traffic Control Centre to safely manage congestion without compromising individual privacy. Experimental results show our system outperforms existing approaches, offering a secure and efficient solution for future ITS.

The development of Connected and Autonomous Vehicles (CAVs), with vehicle-to-everything (V2X) communication technologies, has catalyzed the digital transformation of the vehicle and infrastructure automation industry. These advancements aim, among others, to benefit users by reducing traffic congestion and emissions, enhancing safety, providing comfortable travel, and saving fuel costs. Although many studies have investigated the influence of CAVs on traffic congestion, there exists a lack of governance policies and regulations related to the uptake of CCAM. To fill this gap, we review the regulatory frameworks already implemented in Europe and we investigate through a stated preference survey important aspects related to the barriers of using CAVs. Finally, we analyze the results of the surveys resulting in a well-educated selection of targeted actions that can increase the uptake of CAVs throughout Europe.

Understanding how road operators can harness a paradigm shift in traffic management operations through increased vehicle connectivity, is a key objective of Ireland’s Cooperative Intelligent Transport Systems (C-ITS) pilot project. Transport Infrastructure Ireland (TII) and the European Commission are co-funding the C-ITS pilot deployment including integration into their traffic management system. This is one of the first C-Roads participating countries to integrate C-ITS as part of day-to-day traffic operations. This paper discusses the practical challenges and some of the key activities undertaken to implement and evaluate an effective and interoperable large-scale C-ITS pilot.

Co-operative, Connected and Automated Mobility (CCAM) is of increasing interest to the transport community across the world, though is still maturing. The Horizon Europe project FAME is developing a European framework for testing CCAM on public roads. As part of this, a common evaluation methodology (EU-CEM) is being developed, which provides guidance on how to set up and carry out an evaluation or assessment of direct and indirect impacts of CCAM solutions on different user groups and wider society. Objectives include ensuring that evaluations can be complementary planned with results that are easy to compare, as well as establishing a common vocabulary to support projects in the CCAM community. This paper sets out how the EU-CEM is being developed and embedded into CCAM research in Europe, with a particular emphasis on how the project has adopted an agile and iterative approach to the CEM development alongside meaningful and sustained engagement with stakeholders.

The development of broadband connectivity is a key issue, both for industrial activities and for the performance of the service provided to customers by SNCF Voyageurs. It is a major issue in terms of economic performance, sovereignty, confidence, and sustainability. The introduction of high-speed connectivity should revolutionize the activities of rolling stock maintenance centres, with the development of supervision of assets and robotization of process and supervision, in conjunction with BIM (Building Information Modelling) and digital twin technologies.

Spearheading the adoption of trustworthy AI and cyber security across the triad of automotive, transportation, and logistics embark on rigorous evaluations of applications rooted in open hardware/software ecosystems. Open innovations at chip, embedded and/or system level foster research on security-, safety-, privacy-, and accountability-by-design. Cyber-physical system of systems amplifies the fortress of AI-powered solutions and cyber-physical security, with particular emphasis on open-source frameworks. This paper presents an integrated design flow methodology that incorporates chip, embedded and system-level design and development. The proposed methodology is aligned with the recent trends and state-of-the-art dealing with open-source hardware and software development.

Collective perception (CP) enables connected and automated vehicles (CAVs) to exchange driving environment perception data in order to improve their on-board perception, essentially creating a redundant ‘sensor’ for the CAV and extending its (on-board) Field-of-View (FoV). The use of CP for mixed traffic environments that include CAVs requires a thorough evaluation and validation. Due to the CP large-scale field testing infeasibility and based on the ETSI work, most of the previous work on CP has considered large scale-simulations with a focus on connectivity/network aspects. More recently, large-scale collaborative perception synthetic datasets and open source benchmarks have appeared allowing the perception engineers familiar with CARLA to study CP from a perception point of view missing so far. This work reviews recent achievements in this direction to bridge this gap and motivate future research. As a result of this critical state-of-the-art review, we also produce a set of high-level safety validation requirements for CP testing in simulation, by focusing on urban environments, where non-light-of-sight scenarios hinder the traditional on-board perception task.

EALU-AER is a proposed scalable U-Space ecosystem technology infrastructure integration and demonstration project to establish Ireland’s first Digital Sky Demonstrator (DSD), enabling the smooth transition towards smart cities and is centered at Future Mobility Campus Ireland’s (FMCI) recently established vertiport site, in the vicinity of Shannon Airport, Ireland, therefore inside controlled airspace. The project focusses on deploying a reliable infrastructure, catering U1 and U2 services with higher levels of performance, refinement, and integration to enhanced levels of automated interface with the ATM/ATC, aiming to drive and support U-space regulations and standards development along with global interoperability between Uspace and ATM in a cross-border dimension. This allows for a safe and co-operative integration of zero-emission drones into the airspace that will perform different Urban Air Mobility (UAM) missions. The infrastructure will be facilitated by a mature USpace Services Provider (USSP) platform (WebUAS), a backhaul network for secure data exchange within the ecosystem (ARINC Ground Network (AGN)), Command and Non-payload Communication (CNPC) ground solution (CNPC 5000) and advanced three-dimensional ground surveillance RADAR. The project builds on the reliable integration of these technologies, to provide an autonomous, connected, and collaborative ecosystem ready for U3 and U4 services provision. In this paper, we build the case for the need of such efforts, provide a deep dive into the solution structure the project proposes and describe the operations planned, to validate the system architecture and infrastructure put in place for the same.