Athens is one of the largest economic hubs in Southern Europe. Yet, it faces severe congestion challenges due to the rising demand for car trips and a lack of coordination between modes.
⦁ Improve network connectivity and reduce congestion, by pursuing the urban regeneration project “Grand Walk”, which aims to free up 50.000 square meters of urban space to pedestrians and soft mobility.
TANGENT aims to provide strategies to improve network connectivity and operations through a better information system that connects all modes of transportation. In addition, TANGENT focuses on significantly reducing congestion through the balance of demand and supply in real-time scenarios. The efficiency of TANGENT’s strategies is tested on various future mobility scenarios including CAVs (Connected and Automated Vehicles) and ride sharing services.
TANGENT tools and services applied to the case studies
Service 1: Future Status of the Network
In the case of Athens, since this is a virtual case study, the majority of the TANGENT services can be tested through a number of simulation scenarios. “Current status of the Network” is not relevant since real-time data is not available. However, historical data will be used to perform predictions on future status of the network which will be visualised through the TANGENT Dashboard.
Service 2 Cooperative Incident Management (CIM)
This will be tested through dedicated simulation scenarios to assess the impact of different strategies (response plans) on the recovery of the system when an incident occurs. Using specific triggering conditions regarding network performance indicators the following response plans will be tested:
Service 2 Smart Network Load Balance (SNLB)
Functionalities will be tested under the following triggering conditions:
The response plans that will be activated, based on the triggering conditions, and will be tested through dedicated simulation scenarios are:
The aim of this sub-service is to better balance demand and supply, and to help the network recover from minor incidents. The virtual case study will provide insights on how different response plans affect the performance of the network by measuring specific performance indicators both for the normal conditions (baseline scenarios) and scenarios with the occurrence of planned and unplanned events. The results of this case study may provide a great view on the impact of different strategies as well as a potential mapping between unexpected situations on the network and the corresponding response plans/actions to be implemented by traffic managers.
Finally, “what-if” future scenarios will be tested. The main scenario for the Athens case study aims to answering the following question: “What-if congestion pricing schemes will be implemented on the perimeter of the city centre of Athens?”