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

Jurgen Nieuwenhuijsen, Gonçalo Homem de Almeida Correia, Dimitris Milakis, Bart van Arem, Els van Daalen

Research output: Contribution to journalArticleScientificpeer-review

47 Citations (Scopus)
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Abstract

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.
Original languageEnglish
Pages (from-to)300-327
Number of pages28
JournalTransportation Research. Part C: Emerging Technologies
Volume86
DOIs
Publication statusPublished - 2018

Keywords

  • Automatic vehicles
  • Demand forecasting
  • Innovation diffusion
  • Learning effects
  • System dynamics

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