Ifitroductiofi. Decision making associated with the transport sector is well known for its complexity. Decisions usually have a significant impact on various fields -society, environment, economy etc.- and a large number of individuals or organizations who usually have different priorities and objectives. This specificity very often leads to controversies and disagreements within transport projects making them hard to implement or leading to serious delays. For this reason, it is of great importance to always perform a thorough analysis for considering the implications, for the people or the environment, of proposed actions, especially when there is still an opportunity to modify -or even abandon- a proposal (IAIA, n.d.). This process helps to eliminate the controversy and makes the creation of commonly accepted solutions possible. Many national or international governmental organizations including European Commission (2021) require an impact assessment process in all proposals that are likely to lead to significant economic, environmental or social impacts or when the Commission has a choice between alternative policy options. In the DIT4TraM project we develop control concepts and algorithms with swarm intelligence for the widest possible range of applications, with the four main applications being 1) cooperative connected traffic management, 2) cooperative distributed traffic management, 3) decentralized demand management and 4) cooperation between transport services. All these applications will be tested in practice in the pilots of Bordeaux, Utrecht, Amsterdam, Glyfada, Athens and Barcelona where the gains to be achieved for all the relevant impact areas will be evaluated.
Ifitroductiofi. One of the challenges in assessing urban traffic is developing universal tools that apply to different types of urban forms, transportation systems, drivers’ habits, etc. In this report, we address the assessment methodology for qualitative and quantitative data-driven analyses which is based on the percolation methodology. Here, we use the dynamic percolation process of the traffic loads and the static streets-morphology to develop new, assessment tools for different scales of urban environments. We use network percolation analysis (which examines the robustness of the traffic flow network to failure) to identify street clusters that represent functional modules (i.e. continuous areas where traffic flow is fluent), composed of connected roads with traffic load lower than a pre-defined threshold. We define a fixed threshold for the percolation process (see the methodology section for further elaboration) and focus on the clusters themselves rather than on the links that connect the different clusters. Based on the above, we identify spatially embedded clusters and track their dynamics at different temporal scales, ranging from one hour to a week. By doing so, we present the different patterns of urban mobility, its evolution, and dynamics, and develop new tools for assessing the quality and predictability of urban traffic flow. These tools will be later used on the outcomes of the studied cases and the pilot sites with the aim to produce generic outputs
