Towards a quantitative method to analyze the long-term innovation diffusion of automated vehicles technology using system dynamics

Towards a quantitative method to analyze the long-term innovation diffusion of automated vehicles technology using system dynamics

Nieuwenhuijsen, J.A.H. (TU Delft Transport and Planning)
Correia, Gonçalo (TU Delft Transport and Planning)
Milakis, D. (TU Delft Transport and Planning)
van Arem, B. (TU Delft Transport and Planning)
van Daalen, C. (TU Delft Policy Analysis)

Transport and Planning

2018

This paper presents a novel simulation model that shows the dynamic and complex nature of the innovation system of vehicle automation in a quantitative way. The model simulates the innovation diffusion of automated vehicles (AVs) on the long-term. It looks at the system of AVs from a functional perspective and therefore categorizes this technology into six different levels. Each level is represented by its own fleet size, its own technology maturity and its own average purchase price and utility. These components form the core of the model. The feedback loops between the components form a dynamic behavior that influences the diffusion of AVs. The model was applied to the Netherlands both for a base and an optimistic scenario (strong political support and technology development) named “AV in-bloom”. In these experiments, we found that the system is highly uncertain with market penetration varying greatly with the scenarios and policies adopted. Having an ‘AV in bloom’ eco-system for AVs is connected with a great acceleration of the market take-up of high levels of automation. As a policy instrument, a focus on more knowledge transfer and the creation of an external fund (e.g. private investment funds or European research funds) has shown to be most effective to realize a positive innovation diffusion for AVs. Providing subsidies may be less effective as these give a short-term impulse to a higher market penetration, but will not be able to create a higher market surplus for vehicle automation.

Automatic vehicles
Demand forecasting
Innovation diffusion
Learning effects
System dynamics

http://resolver.tudelft.nl/uuid:e56c997e-8f5e-4545-9b57-8eddd47ba274

https://doi.org/10.1016/j.trc.2017.11.016

2018-05-28

0968-090X

Transportation Research. Part C: Emerging Technologies, 86, 300-327

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2018 J.A.H. Nieuwenhuijsen, Gonçalo Correia , D. Milakis, B. van Arem, C. van Daalen