The airport of the future is expected to become a multimodal connection platform, creating the conditions for travellers to reach their destination by the most efficient and sustainable combination of modes and allowing the airport and its surrounding region to make a better use of their resources. The goal of IMHOTEP is to develop a concept of operations and a set of data analysis methods, predictive models and decision support tools that allow information sharing, common situational awareness and real-time collaborative decision-making between airports and ground transport stakeholders. The specific objectives of the project are the following: 1. Propose a concept of operations for the extension of airport collaborative decision-making to ground transport stakeholders, including local transport authorities, traffic agencies, transport operators and mobility service providers. 2. Develop new data collection, analysis and fusion methods able to provide a comprehensive view of the door-to-door passenger trajectory through the coherent integration of different types of high resolution passenger movement data collected from personal mobile devices and digital sensors. 3. Develop predictive models and decision support tools able to anticipate the evolution of an airport’s passenger flows within the day of operations and assess the operational impact on both airport processes and the ground transport system, with the aim of enabling real-time collaborative decision-making between airports and ground transport stakeholders and enhanced passenger information services. 4. Validate the proposed concept and the newly developed methods and tools through a set of case studies conducted in direct collaboration with airports, local transport authorities and transport operators. The case studies will cover two airports with heterogeneous characteristics and serving different markets, namely the Palma de Mallorca and the London City airports.

Safer urban environments are needed for all road users to ensure the European targets to halve road deaths and injuries by 2030 are met. Vulnerable road users require specific attention in an urban environment that is subject to constant change as new forms of transport and micro-mobility enter the system. Existing traffic simulation models allow changes in traffic conditions to be tested but are often vehicle and travel time focused and do not measure detailed outcomes specific to vulnerable road users and road safety. City administrations and transport managers will benefit from predictive tools that allow these changes and their implications for road safety, mobility, and sustainable transport to be anticipated, and support the associated policy, regulatory and consumer response. The Predictive Approaches for Safer Urban Environments project (PHOEBE) will move beyond the state of the art and deliver an interdisciplinary solution that will integrate traffic simulation, road safety assessment, human behaviour, mode shift and induced demand modelling and new and emerging mobility and telematics data into a harmonised, prospective assessment framework for road safety. New conditions and mobility solutions will be able to be assessed and safe system solutions tested. The PHOEBE framework, software module and knowledge products will allow dynamic safety prediction and socioeconomic evaluation that is evidence-based and simulates future scenarios and impacts. Simple and effective visualisation and socioeconomic modelling will provide the confidence for policy decisions and investment. City administrations across EU will be consulted as the framework is developed and deployed. The feasibility of the framework will be demonstrated in three use cases in Athens (GR), Valencia (ES) and West Midlands (UK), which have been selected to maximise the use of existing base traffic models, potential impact and to ensure the future scalability and transferability of the solution.

Disruptive technologies, such as MaaS and CAVs, are bringing radical changes in urban mobility. The goal of MOMENTUM is to develop a set of new data analysis methods, transport models and planning support tools able to capture the impact of new transport options on urban mobility, in order to support cities in the task of designing the right policy mix to exploit the full potential of emerging mobility solutions. The specific objectives of the project are: 1. Identify a set of plausible future scenarios for the next decade to be taken into account for mobility planning in European cities, considering the introduction of disruptive technologies such as CAVs. 2. Characterise emerging activity-travel patterns, by profiting from the increasing availability of high-resolution spatio-temporal data collected from personal mobile devices and digital sensors. 3. Develop data-driven predictive models of the adoption and use of new mobility concepts and transport solutions, in particular MaaS and shared mobility, and their interaction with public transport. 4. Provide transport simulation and planning support tools able to cope with the new challenges faced by transport planning, by enhancing existing state-of-the-art tools with the new data analysis methods and travel demand models developed by the project. 5. Demonstrate the potential of the newly developed methods and tools by testing the impact of a variety of policies and innovative transport services in different European cities with heterogeneous sizes and characteristics, namely Madrid, Thessaloniki, Leuven, and Regensburg, and evaluating the contribution of the proposed measures to the strategic policy goals of each city. 6. Provide guidelines for the practical use of the methods, tools and lessons learnt delivered by the project in the elaboration and implementation of SUMPs and other planning instruments.

The vision of i4Driving is to lay the foundation for a new industry-standard methodology to establish a credible and realistic human road safety baseline for virtual assessment of CCAM systems. The two central ideas we propose are (1) a multi-level, modular and extendable simulation library that combines existing and new models for human driving behavior; in combination with (2) an innovative cross-disciplinary methodology to account for the huge uncertainty in both human behaviors and use case circumstances. This rigorous treatment of the uncertainty is crucial to assess how much of our confidence in model inputs, parameters, and structure is justified. It also makes explicit how experts from different disciplines judge the outcomes and how justified the underlying assumptions really are. Our consortium combines all the expertise needed to develop this methodology (e.g., traffic engineering, human factors, data & computer science). We have the experimental means to gather the evidence beyond the state-of-art needed to realistically simulate (near) accidents in multi-driver scenarios (access to many data sources, advanced driving simulators, and field labs). We have a strong international network to collaborate with and harmonize our approach with academic and professional partners in e.g., the US (NADS facility); Australia (UQ advanced driving simulator and TRACSLab connected driving simulator facilities), China (Tongji Univ. 8-dof driving simulator and large-scale field lab) and Japan (NTSEL). Finally, we have all the relevant partners on-board to test and apply the methodology (Universities and research labs, OEMs and Tier 1, vehicle regulators, type-approval authorities, standardization institutes, insurance companies). i4Driving offers a proposition for the short and the longer term: a set of building blocks that pave the way for a driving license for AVs.

LEVITATE aims to develop a wide-ranging evaluation framework to assess the impact of connected and automated transport (CAT) on all aspects of transport and individual mobility as well as at societal level. This framework will be used to evaluate the impacts of CAVs on individuals, the mobility system and society using a wide range of indicators. The timescales for the forecasting will include • short term – CAT at an early stage of implementation, technological capability is broadly in line with present day • medium term – CAT becoming more widespread, increasing capability of technologies. Increasing penetration of more highly automated vehicles in fleet • long term – ubiquitous highly integrated transport systems, vehicle fleet is predominantly automated, personal mobility, vehicles and infrastructure have adapted to the new technologies. The outcomes of Levitate will include a set of validated methods to measure the impacts of existing technologies and forecast that of future systems. The methods will be applied to a series of scenarios including those of the present day to provide a range of impact studies of new and future mobility technologies. Based on the Levitate approach a new Connected and automated mobility decision support tool will be developed to provide an evidential basis for future mobility policy-making.