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TopIntroduction
Although Connected and Automated Driving (CAD) technologies are currently being developed and integrated into conventional vehicles, the future pathway to highly or fully automated (SAE Levels 4 and 5 (ANSI, 2018)) and connected vehicles, through complex transition periods, is very uncertain. In this study we develop a system dynamics model of connected and automated vehicle (CAV) uptake, adopting a base model of the transition towards car-sharing and highly automated vehicles (AV) in the Netherlands (Nieuwenhuijsen, Correia, Milkis, van Arem, & van Daalen, 2018). We adapt and extend this model to understand the complexities of the attributes characterizing CAVs through exploring the sensitivities to uptake. Scenarios based on expert opinion and sensitivity testing allow us to examine our research objectives:
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What are the sensitivities of uptake to utility for Level 4-5 CAVs?
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How could IoT based technology services accelerate, enable or enhance CAV uptake?
In this work there is a particular focus on the quality of the “Internet of Things” (IoT), which advanced connected technologies rely upon (characterized by the comfort and safety of the vehicles) as well as the utility these vehicles may provide the user (through comfort, safety, familiarity and attractiveness). We further extend the base model to include selected additional connected technology services (Table 1) that could be offered on a commercial basis in CAVs, by applying learning from public trials and surveys of these services.
Table 1.
Connected technology services
We find that the perceived utility of the CAV or services to the users, and poor quality IoT provision may have the largest impact on CAV uptake pathways. Our conclusions can support the development of business exploitation plans and provide guidance for both local and national policy makers.
TopBackground
System Dynamics (SD) modelling has been widely applied in transportation studies (Shepherd, 2014), including topics such as freight (Kumar & Anbanandam, 2019), inter-city connectivity (Pierce, Shepherd, & Johnson, 2019) and road traffic safety (Kizito & Semwanga, 2020). Of specific relevance to this study, there has been much interest in the automobile industry and uptake of new vehicle technologies. Many of these studies have focused on alternative fuels and powertrain transitions. For example, Struben and Sterman (2008) modelled Alternative Fuel Vehicle uptake in California using a stock and flow system dynamics model, building on the Bass diffusion model Bass (1969) with a social exposure and incorporating an established discrete choice model (Brownstone, Bunch, & Train, 2000). This basic model has been incorporated and extended by numerous authors (Gómez Vilchez & Jochem, 2019), who not only look at uptake but also environmental and societal impacts (often focused around energy/emissions). Other SD models that were developed to understand the uptake of advanced vehicle technologies include in-car navigation (Kim, 2007), car-sharing services (Geum, Lee & Park, 2014) and IoT impact on intelligent transportation (Marshall, 2015).