National Academies Press: OpenBook

The Impacts of Vehicle Automation on the Public Transportation Workforce (2022)

Chapter: Appendix F - Calculator Assumption Descriptions and Rationale

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Suggested Citation:"Appendix F - Calculator Assumption Descriptions and Rationale." National Academies of Sciences, Engineering, and Medicine. 2022. The Impacts of Vehicle Automation on the Public Transportation Workforce. Washington, DC: The National Academies Press. doi: 10.17226/26613.
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Suggested Citation:"Appendix F - Calculator Assumption Descriptions and Rationale." National Academies of Sciences, Engineering, and Medicine. 2022. The Impacts of Vehicle Automation on the Public Transportation Workforce. Washington, DC: The National Academies Press. doi: 10.17226/26613.
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Page 130
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Suggested Citation:"Appendix F - Calculator Assumption Descriptions and Rationale." National Academies of Sciences, Engineering, and Medicine. 2022. The Impacts of Vehicle Automation on the Public Transportation Workforce. Washington, DC: The National Academies Press. doi: 10.17226/26613.
×
Page 131
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Suggested Citation:"Appendix F - Calculator Assumption Descriptions and Rationale." National Academies of Sciences, Engineering, and Medicine. 2022. The Impacts of Vehicle Automation on the Public Transportation Workforce. Washington, DC: The National Academies Press. doi: 10.17226/26613.
×
Page 132
Page 133
Suggested Citation:"Appendix F - Calculator Assumption Descriptions and Rationale." National Academies of Sciences, Engineering, and Medicine. 2022. The Impacts of Vehicle Automation on the Public Transportation Workforce. Washington, DC: The National Academies Press. doi: 10.17226/26613.
×
Page 133
Page 134
Suggested Citation:"Appendix F - Calculator Assumption Descriptions and Rationale." National Academies of Sciences, Engineering, and Medicine. 2022. The Impacts of Vehicle Automation on the Public Transportation Workforce. Washington, DC: The National Academies Press. doi: 10.17226/26613.
×
Page 134

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F-1   Calculator Assumption Descriptions and Rationale This appendix includes a more detailed description and rationale for each of the key work- force effect calculator assumptions made by the research team. Transit Vehicles and Services can be Successfully Automated Assuming transit vehicle automation will eventually be successful is supported by the FTA’s ongoing interest and investment in research, pilot projects, and demonstrations of automated transit services as outlined in the FTA’s STAR Plan (Machek et al. 2018) and FTA’s continu- ing updates to its dedicated transit automation research website (https://www.transit.dot.gov/ automation-research). In addition to the FTA’s support, the transit industry is committed to and interested in technologies that improve safety, operational performance, and service efficiency, as evidenced by recent developments in the industry that manifested an appetite for transit vehicle automation such as the Automated Bus Consortium (www.automatedbusconsortium.com), as well as continued pilot and demonstration projects. The Automated Bus Consortium is “an association of transit and transportation agencies . . . designed to investigate the feasibility of implementing automated bus projects across the United States” (Automated Bus Consortium, n.d.). As of December 2019, the consortium was comprised of 15 member agencies, all of which have contributed time and financial resources to the consortium and have put forward potential candidate projects for testing full-size auto- mated buses. The consortium hoped to jointly procure 75 to 100 full-size, full-speed automated buses, creating the financial incentive for bus OEMs to invest in the research and development activities needed to make automated transit buses possible. Although perhaps a less salient predictor of transit vehicle automation, the continued interest and investment by the private sector in AV technologies also suggested that advancements will continue to be made—increasing the safety and effectiveness of automation technologies while also decreasing their price. Transit Agencies and Passengers will Accept Automated Transit Services Like any new safety-sensitive technology, hesitancy about adopting automated transit vehicles is both understandable and prudent. Many passengers may not initially feel comfortable riding without a human presence on board. Transit agencies may have significant (and valid) concerns about how the absence of an operator might impact the in-vehicle environment, customer per- ception of safety, fare collection, and passenger assistance. The research team recognized this A P P E N D I X F

F-2 The Impacts of Vehicle Automation on the Public Transportation Workforce reality but, again, had to assume the use cases, as designed and implemented, would be accepted by passengers and embraced by transit agencies to estimate the potential impact of the use cases on the transit workforce. Workforce Effects were Calculated as if They Happened Immediately Although transit service automation will definitely change the composition of transit jobs and alter the number of labor hours needed to deliver each hour of revenue service, the combined results of these changes on jobs are complex and difficult to predict and are further moderated both by the current number of vacant positions and the number of employees on the verge of retirement. Natural attrition may absorb a significant amount of potential job displacement (USDOT 2021), and it is likely that new jobs will be created from the increased productivity that automation allows (USDOT 2021). There were also many forces that could slow or even completely eliminate any appreciable job losses, including current force and wage reduction rules in collective bargaining agreements, the need for transit agencies to provide fair and equitable protections to transit employees under 13(c), or even a transit agency’s desire to avoid employee layoffs or wage reductions simply to avoid negative impacts on its employees. However, the research team proceeded with calculating the potential workforce effects, including the number of affected jobs and possible changes in FTEs, as if the use cases were implemented today and as if employee protections were not in place. This approach to estimat- ing the workforce effects helped depict the potential unmitigated workforce effects of transit service automation. Vehicle Electrification Workforce Effects were Separate Many advocates of vehicle automation believe that vehicle electrification is an additional necessary supporting technology; however, electrification of vehicles is not a necessary pre- condition for the automation of vehicles. For example, LILEE Systems developed and success- fully tested a system to automate a 9-meter (approximately 30-foot) diesel bus in Taichung City in Taiwan (LILEE Systems 2019). Whether electrification and automation occur together hinges on several factors, including the rate of diffusion of EVs, which depends heavily on regulation (e.g., emissions standards) and the price of batteries. However, for this study, because automa- tion and electrification may—but do not necessarily have to—occur together, and because this study was on the workforce effects of automation, the research team did not include the poten- tial workforce effects of bus fleet electrification. Agency Types will have Different Adoption Likelihoods The transit industry is not homogenous, and there are many notable differences across regions and transit agency types that might influence the likelihood that automated transit service is adopted. The research team used the agency type categories of rural, small urban, and large urban to differentiate transit agencies from one another. The transit automation differences between rural, small urban, and large urban transit agencies were backed up by a 2019 study that surveyed 258 transit agencies about their knowledge of and plans for transit vehicle automation (Godavarthy 2019). • Rural and small urban transit agencies were less likely to know about the SAE levels of vehicle automation (see Figure F-1).

Calculator Assumption Descriptions and Rationale F-3   Source: Godavarthy (2019, 9). Figure F-1. Awareness of SAE levels of vehicle automation. Source: Godavarthy (2019, 10). Figure F-2. Familiarity with fully automated shuttles. • Rural and small urban transit agencies were less likely to know about fully automated shuttles (see Figure F-2). • Most rural transit agencies were not interested in operating automated shuttles now or in the near future (see Figure F-3). • Rural agencies appeared generally to have a longer timeline for their plans to potentially procure transit vehicles with some level of automation (see Figure F-4). Although there were many factors underlying these agency type differences, a few important considerations stand out: • Rural transit agencies often executed their vehicle procurements through their state DOTs. If the state DOT is not interested in using funds to explore automated transit vehicles, then the rural agency likely will not be able to procure automated transit vehicles (Godavarthy 2019).

F-4 The Impacts of Vehicle Automation on the Public Transportation Workforce • Although funding was a challenge at all transit agencies, rural and small urban agencies often faced continual funding challenges and may also lack dedicated sources of local funding, which were often the most flexible to use for testing and innovation of new technologies and services. • Rural agencies’ riders were often composed of higher proportions of seniors and people with disabilities—people who might feel less comfortable riding or might be unable to ride in an autonomous vehicle (Godavarthy 2019). Source: Godavarthy (2019, 18). Figure F-4. Timeline for procuring transit vehicles with some level of automation. Source: Godavarthy (2019, 11). Figure F-3. Does your agency currently operate, or plan to operate in the near future, fully automated shuttles?

Abbreviations and acronyms used without de nitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FAST Fixing America’s Surface Transportation Act (2015) FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration GHSA Governors Highway Safety Association HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TDC Transit Development Corporation TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S. DOT United States Department of Transportation

Transportation Research Board 500 Fifth Street, NW Washington, DC 20001 ADDRESS SERVICE REQUESTED ISBN 978-0-309-68685-3 9 7 8 0 3 0 9 6 8 6 8 5 3 9 0 0 0 0

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Advancements in the automation of transit vehicles will likely have significant impacts; however, the possible effects on the public-transportation workforce is largely unknown. This is due partly to the fledgling state of transit vehicle automation and partly to the significant amount of uncertainty about how and when automated transit services become more prevalent.

The TRB Transit Cooperative Research Program's TCRP Research Report 232: The Impacts of Vehicle Automation on the Public Transportation Workforce provides an analysis of the possible impacts of automation on the public transportation workforce.

Supplemental to the report are:

· Staffing Count Survey

· APTATech Workshop Presentation

· Workshop Notes

· Employee Survey

· Survey Flyer

· Industry Webinar Presentation

· Industry Poll Data

· Task Impact Ratios, and

· Workforce Effect Estimates.

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