Forecasting Americans' Long-Term Adoption of Connected and Autonomous Vehicle Technologies

By Prateek Bansal, Kara M. Kockelman. 2016

718 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/28c7cbd1e08b184a0208a1c9fd980eb50d05af97

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In Automotive Engineering

In Electrical and Electronic Engineering

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory (2020, background): “Existing studies predict that by 2045, AVs (speci ﬁ cally Level 5 automation without human intervention) may be adopted by 24.8 – 87.2% of the industry (Bansal and Kockelman 2017).”
Capturing the behavioural determinants behind the adoption of autonomous vehicles: Conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars (2019, background): “Previous studies (see e.g. Woldeamanuel and Nguyen, 2018; Sanbonmatsu et al., 2018; Daziano et al, 2017; Haboucha et al., 2017; Nair et al., 2017; Bansal et al., 2016; Lavieri et al., 2017; Bansal and Kockelman, 2017; Lavasani et al., 2016; Kyriakidis, et al., 2015; Adnan et al., 2018; Umberger, 201”
Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China (2019, methodology): “Existing research on AV mainly focuses on macro-level analysis, including policy issues [7,16], legislative supervision [17], SAV dispatch optimization [5,18,19] and the impact on traditional transportation system [5,20,21].”
Are we ready to embrace connected and self-driving vehicles? A case study of Texans (2016, methodology): “…from the Texas Department of Transportation, and a recent survey of Americans showing Texans to offer a balanced representation of U.S. responses (Bansal and Kockelman 2016), a Texas-wide survey, asking 93 questions distributed in seven sections, was disseminated through Survey Sampling…”
A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method (2017, background): “Therefore, as CAV market penetration rate ( Bansal and Kockelman, 2017; Chen et al., 2016; Lavasani et al., 2016; Hadi et al., 2016 ) and platooning intensity increase in the future, traﬃc operators have to be aware of possible impacts of different CAV technologies on capacity.”
External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions (2019, background): “The adoption rate of AVs is expected to increase in the upcoming decades (Bansal & Kockelman, 2017).”
A multi-level model on automated vehicle acceptance (MAVA): a review-based study (2019, background): “…and security include equipement and system failure, cyber security or the fear of terrorism and hacking, and system performance in poor weather and terrain and unexpected conditions (e.g., Bansal & Kockelman 2017; Dong et al. 2017; Gurumurthy 2017; Penmetsa et al. 2019; Tussyadiah et al. 2017).”