PAsCAL D7.4 Long term impact analysis with a system-dynamics model

The aim of PAsCAL is to develop a holistic, user-centric Guide-to- Autonomy concept aimed at accelerating the user-friendly evolution of connected and automated vehicles (CAVs) and transport systems. In doing so, it addresses important issues relating to the role of humans in this evolution, ranging from real-time driving control to long-term training needs for jobs, in particular appropriate interactions of the autonomous vehicles with different road users including disabled people and non- drivers.

PAsCAL carried out a set of thorough surveys (e.g. online, face-to-face interviews) on public acceptance (WP3), simulated driving scenarios (WP4), training and education (WP5), and real-world demonstrations (WP6). WP7 brought together the results from all these previous WPs and carried out a systematic and detailed analysis of user behaviour, and assess the potential impacts of various levels of user acceptance on CAVs, and support decision makers in considering the pros and cons of future CAV solutions.

With impact areas and pathways of CAVs identified in D7.1, impact indicators reviewed in D7.2, and knowledge inputs on user acceptance from D7.3 and WP3, this deliverable (D7.4) represents work carried out in Task 7.4 in which a System Dynamics (SD) based model was developed to simulate the diffusion of CAVs and its impacts over a 50-year period, using the UK as a case country, to explore how users’ perception, CAV technological advance and CAV utilities affect user acceptance and CAV diffusion, the wider mobility and society impacts of CAV diffusion, and the dynamic relationships between all these factors.

The SD model adopted Bass innovation diffusion theory which considers two types of acceptance, driven by desire to innovate and by need to imitate the rest of the society. CAVs in three modes were considered: CAV private car, CAV car/ride sharing and CAV bus, and potential users who accept CAVs will choose between them. Key CAV diffusion indicators calculated in the SD model include CAV technology advance, number of CAV users, CAV fleet size and CAV market penetration. The model also calculates indicators that reflect wider impacts of CAVs. Key CAV impact indicators include average travel time, average travel cost, mode share, Vehicle Miles Travelled (VMT), energy intensity, carbon emissions, and traffic accidents. Six scenarios, i.e., marketing campaign, training campaign, Research and Development (R&D) investment increase, CAV overall boost, CAV shared mobility boost and CAV public transport boost,

in additional to a base scenario, were tested, to assess the long-term impacts of possible interventions that are designed to stimulate CAV diffusion and to optimise CAV impacts.

The results suggest that without interventions CAV diffusion will be slow in the beginning and then start to increase rapidly from around 2035. After an S-shaped growth it will reach market saturation of 98% in around 2057.

CAV diffusion will lead to reductions in average travel time, average travel cost, carbon emissions and traffic accidents.

Training campaign, which prepares people to be ready for CAVs when they need to imitate existing users, is more effective in accelerating CAV diffusion than marketing campaign, which encourages innovators and early adopters to adopt CAVs out of their desire to innovate. Promoting shared CAVs and CAV public transport can contribute to more sustainable and more affordable mobility with CAVs, although this may lead to smaller CAV market size in terms of CAV sale.

The results were used to develop policy recommendations which will feed into the Guide2Autonomy in WP8.