Abgeschlossene Forschungsprojekte

Auftraggeber / Contracting authority

Europäische Union (Horizon 2020)

Projektpartner / Project partners

• Rupprecht Consult GmbH, Cologne, Germany
• PTV Planung Transport Verkehr AG, Karlsruhe, Germany
• Polis, European Cities and Regions Networking for Innovative Transport Solutions, Brussels, Belgium
• FEHRL - Forum of European National Highway Research Laboratories, Brussels, Belgium
• iD4CAR, Rennes, France
• VEDECOM - Institute for Energy Transition (ITE), Versailles, France
• Renault, Versailles, France
• TASS International, Helmond, Netherlands
• VTI - Swedish National Road and Transport Research Institute, Linköping, Sweden
• University of Florence, Florence, Italy
• University of Stuttgart, Department for Transportation Planning and Traffic Engineering of the Institute for Road and Transportation Science (VuV), Stuttgart, Germany
• City of Gothenborg, Gothenburg, Sweden
• City of Helmond, Helmond, Netherlands
• City of Milton Keynes, Milton Keynes, United Kingdom

Laufzeit / Duration

05/2017 – 04/2020

Website

• CoEXist Homepage
• Project overview on the website of the European Commission - INEA

Ansprechpartner / Contact

• Prof. Dr.-Ing. Markus Friedrich
• Dipl.-Ing. Manfred Wacker
• Jörg Sonnleitner, M.Sc.

Overview

The mission of CoEXist is to systematically increase the capacity of road authorities and other urban mobility stakeholders to get ready for the transition towards a shared road network with increasing levels of connected and automated vehicles (CAVs), both in terms of vehicle penetration rates and levels of automation using the same road network as conventional vehicles (CVs). CoEXist will enable mobility stakeholders to get “Automation-ready” – which CoEXist defines as conducting transport and infrastructure planning for automated vehicles in the same comprehensive manner as for existing modes such as conventional vehicles, public transport, pedestrians and cyclists, while ensuring continued support for conventional vehicles on the same network.

Approach

Automation-ready transport and infrastructure planning in cities is a key precondition for fulfilling the promises of CAVs to reduce road space demand and improve traffic efficiency and safety. CoEXist will address three key steps in transport and infrastructure development:

•  Automation-ready transport modelling: Validated extension of existing microscopic and macroscopic transport models to include different types of CAVs (passenger car/ light-freight vehicle, automation levels).
• Automation-ready road infrastructure: Tool to assess the impact of CAVs on safety, traffic efficiency and space demand and development of design guidance for hybrid (CAV-/CV-shared) infrastructure.
• Automation-ready road authorities: Elaboration of eight use cases in four road authorities (Gothenburg, Helmond, Milton Keynes and Stuttgart), used to evaluate AV impacts on safety, traffic efficiency and road space requirements (with CoEXist tools) and making detailed hybrid infrastructure design recommendations.

Overall

CoEXist’s strategic aim is to bridge the gap between connected and automated vehicles (CAVs) technology and transportation and infrastructure planning by strengthening the capacities of urban road authorities and cities to plan for the integration of CAVs in the same network.

CoEXist will achieve its aim through the following project objectives:

• To define requirements of stakeholders in the transition towards Automation-ready transport and infrastructure planning;
• To develop and validate microscopic and macroscopic modelling tools (PTV Vissim and Visum) that enable the transition towards Automation-ready transport planning;
• To develop a hybrid infrastructure impact assessment tool that enables the transition towards Automation-ready road infrastructure planning;
• To demonstrate CoEXist tools in road authorities and cities;
• To ensure take-up and exploitation of CoEXist results through dissemination.

Our main focus within the project

• Developing tools (plugins, extensions) for macroscopic modelling software (PTV Visum) to be able to model CAV and incorporate their characteristics in macroscopic travel demand models.
• Apply modelling tools to the Stuttgart Region travel demand model and examine the impacts of CAV with different capabilities and market penetrations within two use cases:
  • Impacts of CAV on travel time and mode choice on a network level:
    This use case focusses on the transition from 0% to 100% CAV. CAV are assumed to be highly automated and not yet able to perform driverless.
  • Impacts of driverless car- and ridesharing services:
    This use case focusses on new mobility services using driverless CAV. These services could be integrated into or compete with public transport.