The paper proposes a general framework for the assessment of the impacts of the introduction of Connected and Automated Transport Systems (CATS) on traffic. The main objective is to address the question of scalability and transferability of the identified impacts of Autonomous Vehicles (AVs) in particular, focusing on network performance of urban areas. A combination of microscopic and macroscopic simulations as well as statistical methods are applied. Microscopic simulation is conducted to measure the changes in network capacities by utilizing the concept of the Macroscopic Fundamental Diagram (MFD), under different AV penetration rates. The resulting capacities are used to estimate the effects on the Passenger Car Units (PCU) under different AV penetration rates and derive functional relationships, which are further introduced to travel demand models to forecast the macroscopic impacts on network performance. The results indicate a positive impact in relation to capacity changes resulting from the presence of AVs which vary with penetration rate. Analysis of three different urban networks, Barcelona, Bilbao (Spain), and Athens (Greece), reveals consistent trends. However, notable differences are observed on the estimated PCUs for Athens, potentially because of the different mixed-traffic composition. Further exploration of the critical AV modeling specifications and network characteristics is therefore required for deriving transferable PCU functional relationships across networks. Nevertheless, the static assignment results verify the expected trends in network performance impacts in relation to the applied PCU relationships. Finally, the transferability of the proposed methodology across networks is demonstrated.

Decisions on transport policy measures have long-term and important impacts on the economy, environment and society. Transport policy measures can lock up capital for decades and cause manifold external effects. In order to allow policymakers to evaluate transport policies, the developed decision support tool facilitates the evaluation of the multidimensional impacts of the implementation of transport policies. The objective of the decision support toolset presented in this paper is to support transportation planning and design practices based on an integrated transportation analysis of the area of examination to determine the most applicable combination of mobility services. This paper provides a comprehensive description of the interactive decision support tool implemented to help cities and decision makers design their strategies and shape the urban mobility of the future.

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.

Predicting the future supply and demand of a transport network are challenging and important problems in real-time traffic management systems that are essential to enhance the decision-making process for deploying adequate traffic strategies under different conditions (e.g., road works, accidents). In the context of the TANGENT H2020 project, simulation-based and data-driven methodologies are developed focusing on the real-time demand and supply prediction problems. This paper focuses on the development and integration of the demand and supply models as well as incident detection methods into traffic simulation environments for network-wide traffic predictions. The role of each component of the framework and their interoperability is explained in the paper, using as testbed the network of Athens, Greece.