Final Report Summary - ITS4SIT (Intelligent Transportation Systems for Safer and Improved Traffic) The main objective of the project was to develop a framework for Intelligent Transportation Systems that would allow inter-vehicle coordination, to deal with local interactions, as well as vehicle-to-infrastructure coordination, to deal with more global and system-wide interactions. To achieve this, a three layered framework has been developed. This defines three levels (micro, meso and macro) which are independent of each other, but when combined, they are capable of offering a better performance of the system, leading to a more sustainable and efficient use of the road infrastructures. The micro level deals with a single vehicle, and how it interacts with the environment; the meso level accounts for interactions of (dynamic) groups of vehicles (e.g. to form a road train, junction crossing coordination...); the macro level is concerned with system-wide goals (e.g. minimise gas emissions, avoid congestions...). The framework has been developed from the perspective of self-adaptive and self-organising systems and electronic institutions, and it has taken much inspiration from Elinor Ostrom's work on self-governing institutions for common resources. For instance, the problem of parking allocation (i.e. what parking slot to allocate to what driver depending on her spatio-temporal requirements) has been formalised as a common good problem. By taking a resource allocation problem perspective, the Fellow has been able to develop a framework that is not only applicable to the transportation domain, but also to other more general problems of resource provision and appropriation (e.g. grid computing, smart grids, etc.). Moreover, the research carried out during the project has led the Fellow and the Scientist-in-charge first to consider issues of fairness in resource allocation, and later to have a much wider vision and study other concepts of justice in such systems. This has been materialised by devising a whole new research programme in Computational Justice, an inter-disciplinary investigation at the interface of computer science and social sciences (e.g. philosophy, economics, psychology and jurisprudence), enabling and promoting an exchange of ideas and results in both directions. From one perspective, computational justice is concerned with the study of formal representations of justice developed in computer science, and transferring them to social settings. From the other perspective, it is also concerned with importing concepts from the social sciences into computing applications. Although the study of Computational Justice had not been foreseen nor planned in the project proposal, it is one of the major achievements of the project, since it has application to (and implications in) many socio-technical systems (i.e. systems composed of ICT infrastructure, devices and humans interacting with them, such as ITS or Smart Grids). While there is an extensive work on the more "technical side" of such systems, the relationship between (human) users and the system has not been fully studied, although it is a crucial aspect of the success of such systems. Thus, we believe that the inclusion of justice concerns in these systems will be necessary for the success both in their deployment and acceptance. Actually, Computational Justice goes beyond only resource allocation problems, and it can have numerous applications, such as evidence based policy making (e.g. helping policy makers design the appropriate rules to achieve some goals), dispute resolution (e.g. design procedures to solve conflicts) or managing Big Data (e.g. how to organise, access, control the huge amount of data received from the numerous sensors in our environment), amongst many others.The work done so far on Computational Justice has been to lay some of its foundations, and also the formalisation of some particular instantiations of justice. However, the potential for other work is immense, and during this project we have merely taken the first steps into a whole new area of research.