As the United States transitions to automated vehicles, millions of American jobs will be impacted directly through the replacement of workers and changes in job requirements needed to work with and maintain automated vehicles, as well as indirectly through changes in organizations, standards of living, and worker well-being. The transition to automated vehicles will impact the employment pipeline, pay for driving occupations, and quality of life for drivers. These interrelated and potentially pervasive economic, social, and policy changes require interdisciplinary, collaborative approaches to examine who, how, and why workers and society will be impacted by this transition. This knowledge will prepare organizations, workers, and policymakers for workplace changes brought by this potentially disruptive technology and inform policy changes needed to deal with the indirect impacts of these workplace changes on society. The long-term goal of this research study is to prepare the current and future driving workforce for the shift that will occur as broader automated vehicle dissemination occurs and eventual automated vehicle saturation is seen in the United States.
The investigators draw from organizational psychology, economics, sociology, geography, technology, and transportation engineering to analyze who, why, and how two driving occupations (taxi/ride hailing and heavy trucking and tractor trailers) will be affected by automated vehicles. The investigators utilize a mixed-method approach focusing on drivers, supervisors, and management to examine: (1) How will driving jobs change in response to automation of vehicles and what new skills will be required? (2) How willing and able are workers to adapt to the changing nature of driving jobs, and will the changing nature of jobs disadvantage some groups of workers more so than others? (3) What are the anticipated downstream impacts on drivers (e.g., employment trends and income inequality) in the transportation industry, organizations, and society? Focus groups, surveys, and skill mapping techniques will be used to identify driving occupations most at risk for displacement and the occupations that will require extensive retraining due to automated vehicle adoption. The skill maps and secondary occupational data will be incorporated with technology diffusion models to estimate adoption levels across the two driving contexts. These data will also be used to estimate economic models to understand job loss, wage reductions, and the impact of skills changes in the driving contexts of interest on society and income inequality. Skills maps will be disseminated to workforce and education groups who can develop job training and certificate programs to mitigate workforce displacement and help workers obtain workforce training and reskilling in the age of automated vehicles. Results of this project will be disseminated to the broader community via visits to area high schools and a YouTube channel to share webinars and training videos with stakeholders.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
