To ensure the effective accomplishment of its strategic objective, CoEXist addressed three key steps in transport and infrastructure planning:
• Automation-Ready Transport Modelling: developed and validated extensions of existing microscopic traffic flow simulation and macroscopic transport modelling tools to include different types of CAVs (passenger cars/light-freight vehicles with different automation levels).
• Automation-Ready Road Infrastructure: developed and implemented tools to assess the impact of automated vehicles on traffic efficiency, space demand and safety, and provide guidance on infrastructure development, to suit both conventional and automated vehicles.
• Automation-Ready Road Authorities: elaborated eight strategically-selected use cases in four local authorities (Gothenburg, Helmond, Milton Keynes and Stuttgart), used to evaluate – with the CoEXist tools – the impacts of automated vehicles on traffic efficiency, road space requirements and safety, to guide local policy discussion and identify strategies to improve automation-readiness.
Through this approach, CoEXist has delivered tools for a structured approach of assessing future scenarios and handling uncertainties, including not only the described modelling and impact assessment, but also an Automation-ready Framework, which aims to guide cities in their planning processes to address CCAM.
It is the ambition of CoEXist that it's research results and analysis should influence the transport and infrastructure planning in road authorities and steer their progress towards automation-readiness. Therefore, each CoEXist road authority has developed a concrete ‘Automation-ready Action Plan’, providing detailed guidance on their specific processes and steps that should be taken to conduct automation-ready transport and infrastructure planning. To develop its action plans, each city has reflected on the results from their Automation-ready Forum – participative instances for discussion and engagement with citizens and key local stakeholders – and the conclusions from the use cases’ impact assessment. CoEXist’s ‘Guidelines: How to become an automation-ready road authority?’ provide a full description on how to implement the tools and methodologies developed by the project, and summarises the key conclusions and lessons learnt from the project’s use case implementation and analysis.
Results of CoEXist’s use case evaluations provided evidence for the opportunities of automation as well as for risks of a potential deterioration of urban mobility, especially at the initial stages of CAV deployment. Findings show that inserting CAVs in traffic does not necessarily improve efficiency. It heavily depends on penetration rate, driving logic and spatial conditions applying. Only for higher stages of penetration, combined with more advanced CAVs, will CCAM implementation start to generate some improvements in urban mobility. In addition, significant conclusions were derived about the effects of CAVs on different types of road infrastructure. CoEXist found that decreased traffic performance is to be expected in the introductory stage due to the cautiousness of AV-behaviour. However, the magnitude of the decrease varies substantially between the investigated sites, with large decreases in traffic performance for less structured traffic environments (like roundabouts and shared space), and smaller decreases for highly structured traffic environments (such as highways, arterials and signal-controlled intersections). Changes in travel time perception can lead to increased car usage, longer travel distance and time per trip. This conflicts with sustainable development goals and should be addressed by policies (e.g. change current car ownership paradigm). Besides, the conducted safety assessments indicate potential reduction of road traffic crash risk and that the benefits increase with increased penetration rate of automated vehicles and more advanced automation functions. The results indicate that different types of automation functions may reduce the risk for some type of accidents and that these gains may arise already at lower level of automation.
