Stick Shift: Autonomous Vehicles, Driving Jobs, and the Future of Work

More than 30 companies say they are just a few years away from introducing autonomous vehicles to the mass market. While it is unknown what the ultimate impact of autonomous vehicles will have on jobs, there is a possibility that there could be a relatively rapid transition. This is likely to cause significant pain in a number of communities, as well as exacerbate the losses of "good jobs," a category that includes some driving jobs. It would be prudent to strengthen our safety net and labor market to absorb a shock from autonomous-vehicle technology, as well as ensure that autonomous-vehicle technology is safe and reliable. This will be a challenge, given the recent change in the party controlling the executive branch, and its new secretary of transportation. Strengthening the unemployment insurance system, improving apprenticeship programs, making higher education more affordable, and committing to full employment can not only minimize the harm to displaced workers, but can provide them with opportunities that lead to fulfilling and economically sustaining jobs. This is good policy whether or not autonomous vehicles are around the corner

What’s Behind Recent Transit Ridership Trends in the Bay Area? Volume I: Overview and Analysis of Underlying Factors

Public transit ridership has been falling nationally and in California since 2014. The San Francisco Bay Area, with the state’s highest rates of transit use, had until recently resisted those trends, especially compared to Greater Los Angeles. However, in 2017 and 2018 the region lost over five percent (>27 million) of its annual riders, despite a booming economy and service increases. This report examines Bay Area transit ridership to understand the dimensions of changing transit use, its possible causes, and potential solutions. We find that: 1) the steepest ridership losses have come on buses, at off-peak times, on weekends, in non-commute directions, on outlying lines, and on operators that do not serve the region’s core employment clusters; 2) transit trips in the region are increasingly commute-focused, particularly into and out of downtown San Francisco; 3) transit commuters are increasingly non-traditional transit users, such as those with higher incomes and automobile access; 4) the growing job-housing imbalance in the Bay Area is related to rising housing costs and likely depressing transit ridership as more residents live less transit-friendly parts of the region; and 5) ridehail is substituting for some transit trips, particularly in the off-peak. Arresting falling transit use will likely require action both by transit operators (to address peak capacity constraints; improve off-peak service; ease fare payments; adopt fare structures that attract off-peak riders; and better integrate transit with new mobility options) and public policymakers in other realms (to better meter and manage private vehicle use and to increase the supply and affordability of housing near job centers)

Human versus automated agents: how user preferences affect future mobility systems

Along with rapid advancements in digital, and physical technologies, shared autonomous electric vehicles are forecasted to gradually complement and replace traditional human-based mobility systems. Information systems play a key role in such a deep socio-technical system to pave the path toward a more sustainable future. This study investigates a hybrid ride-hailing platform of automated and human-driven vehicles. Our focus lies on the demand side where we evaluate the influence of user behaviors on economic and environmental system performance. For this, we employ a data-driven agent-based simulation modeling heterogeneous vehicle and user agents calibrated by rental data of a leading vehicle-sharing company. Our findings declare that diverse customer responses to the introduction of shared autonomous electric vehicles yield significantly different fleet performance and ecological costs. We also observe that the status quo customer communication design of ride-hailing platforms need adjustments to maximize the potentials of future hybrid shared mobility systems