Modelling Uptake Sensitivities of Connected and Automated Vehicle Technologies

Gillian Harrison, Simon Shepherd, Haibo Chen

OAI: oai:igi-global.com:273181 • DOI: 10.4018/IJSDA.2021040106

Connected and automated vehicle (CAV) technologies and services are rapidly developing and have the potential to revolutionise the transport systems. However, like many innovations, the uptake pathways are uncertain. The focus of this article is on improving understanding of factors that may affect the uptake of highly and fully automated vehicles, with a particular interest in the role of the internet of things (IoT). Using system dynamic modelling, sensitivity testing towards vehicle attributes (e.g., comfort, safety, familiarity) is carried out and scenarios were developed to explore how CAV uptake can vary under different conditions based around the quality of IoT provision. Utility and poor IoT are found to have the biggest influence. Attention is then given to CAV ‘services' that are characterized by the attributes explored earlier in the paper, and it is found that they could contribute to a 20% increase in market share.

Adaptive & Complex Systems Computer Science and Information Technology Computer Engineering