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'AV-Ready' transport models and road infrastructure for the coexistence of automated and conventional vehicles

Periodic Reporting for period 2 - CoEXist ('AV-Ready' transport models and road infrastructure for the coexistence of automated and conventional vehicles)

Reporting period: 2018-11-01 to 2020-04-30

CoEXist understands that automation-ready transport and infrastructure planning in cities are a key precondition for fulfilling the promises of connected and automated vehicles (CAVs) and effectively handling related risks. This need for considering CAVs in local policy discussions and planning processes (e.g. SUMP), should not however be misunderstood as endorsing the disruptive technologies surrounding CCAM and their impacts, but rather empowering the local authorities to critically review the anticipated technological changes and shape the future according to their expectations.

Consequently, the project’s strategic aim was defined as: “to bridge the gap between automated vehicles (AVs) technology and transportation and infrastructure planning, by strengthening the capacities of urban road authorities and cities to plan for the effective deployment of AVs”. This is defined in the CoEXist project as achieving ‘automation-readiness’, i.e. the capability of making structured and informed decisions about the comprehensive deployment of CAVs in a mixed road environment.
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.
These findings highlight the importance of proactive action from authorities to plan for the transition phase, and the need for further research and policy development. By focusing on the (preliminary) certainties derived from modelling and empirical assessments of CAV impacts, CoEXist aims to facilitate decision-making and the identification of preparatory measures and policies of ensured benefit for cities and that facilitate CCAM implementation during the transition phase, creating quick co-benefits.

Among this, CoEXist highlights the importance of transforming planning practices from the ‘predict then act’ paradigm, towards agile and adaptive decision-making, aided by capacity development for local authorities, robust scenario simulations, and cross-sectoral cooperation, and the development of integrated legal and policy framework to regulate CAV deployment and service provision.

Cities should also take incremental steps towards the deployment of CCAM-services, upgrading motorways and regional roads to support mixed traffic with enhanced infrastructure, further developing C-ITS capabilities (e.g. I2V), enabling support to AVs and conventional vehicles, while optimising traffic management. In this way, authorities can effectively regulate where CAVs are allowed to operate and how the deployment should take place, by enhancing public-private cooperation to develop business models that prioritise CCAM based on collective, car and ride sharing services, responding to real societal needs and contributing to sustainability goals.

To do so, it is important to define a clear vision for CCAM, materialised in realistic and measurable targets, that will enable effective expectation management. Cities should also be aware of the various opportunities, and challenges that arise from CCAM deployment, considering its potential role in transforming travel behaviour, and facilitating modal shift towards integrated public or shared transport services. A structured and well-informed decision-making process, through holistic frameworks, is required to ensure sustainable and affordable services that align with local policy goals and respond to user needs.