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

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1   Vehicle automation is likely to have significant impacts across many different industries, including public transportation. In the public transit industry, transit vehicle automation has generated research and innovation; however, many challenges remain. Progress toward automated transit service also leads to important questions about the potential workforce effects transit vehicle automation may have. To date, the research on potential workforce effects remains quite sparse—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 will actually be operated. This research, sponsored by the Transit Cooperative Research Program (TCRP) as Project J-11/Task 34, “The Effects of Vehicle Automation on the Public Transportation Workforce,” seeks to fill that gap in automation-related workforce impact research by (a) identifying likely transit automation use cases, (b) analyzing each use case’s potential effects on the public transit workforce, and (c) identifying clusters of strategies to prepare the workforce for and mitigate negative effects of transit vehicle automation. This research estimated the potential workforce effects of five transit automation use cases on five directly affected operations jobs in the transit industry, including opera- tors, dispatchers, supervisors, mechanics, and service persons. The five use cases were (1) bus automation for maintenance and yard operations, (2) automated low-speed shuttles, (3) automated bus rapid transit (BRT), (4) automated mobility on demand (MOD), and (5) automated local bus transit. Workforce effect estimates were calculated for all five use cases using a workforce effect calculator (see Figure 1) that factored in the possible changes in job tasks that would be caused by the adoption of automated transit services. Within each use case, workforce effect estimates were calculated for both a remote and in-person operational model for automated services and the partial or full adoption of the use case. Assumptions made in the workforce effect calculator, including how and to what extent automated transit services would be implemented, were based on industry engagement activities (i.e., an interactive workshop and two webinars). To produce workforce effect estimates, the research team took the steps shown in Table 1. The calculator produced four key outputs: • Current employees: The number of full-time equivalent (FTE) employees currently working with the conventionally driven transit service that the use case could replace. Calculated based on the type of conventionally driven service to which the use case applied (e.g., low-speed automated shuttles applied to fixed-route feeder and/or circulator services). • Jobs gained (lost): The change in the number of FTEs that will result when the use case replaces conventionally driven transit services at the transit agencies that implemented S U M M A R Y The Impacts of Vehicle Automation on the Public Transportation Workforce

Figure 1. Workforce effect calculator. Step Title Description 1 Estimate employee counts per job and mode group The National Transit Database did not have employee counts for all five directly affected jobs. The Texas A&M Transportation Institute (TTI) estimated the employee counts (see Section 4.1.2 for a description of the estimation methodology). 2 Determine use case—service type applicability Only apply use cases to the current types of service they appear most likely to impact based on current deployments, industry feedback, and professional judgment. 3 Build impact tree Create an impact tree to determine the portion of employees who could be affected by the use case. An impact tree is a series of ratios, applied to current transit service that ultimately results in an estimate of the percentage of current conventionally driven services that would be replaced with the use case. The percentage of replaced current service is equivalent to the percentage of effected current employees. 4 Create concepts of operations To generate estimates of how individual job tasks would be affected by the implementation of a use case, the research team developed a basic concept of operations for both the in-person and remote operational models. 5 Build task lists and task-effect ratios Under each operational model and use case, estimate the percentage change in time that each task would account for if the use case was implemented. Table 1. Workforce effect estimation steps.

Summary 3   the use case. Calculated based on the use case’s net impacts on the time required to perform job tasks. • Remaining affected jobs: The number of FTEs that remain in their jobs after the use case is implemented and replaces conventionally driven service. These employees will experi- ence task changes that may result in altered job duties; work shifts; and knowledge, skills, and abilities (KSAs). These employees may also need training to perform their new duties. Calculated as the sum of affected jobs and any jobs gained or lost. • Unaffected jobs: The number of FTEs that remain in their jobs and are unaffected by implementation of the use case. These employees will not experience any direct changes in their tasks. Table 2 summarizes the results, displaying the estimated workforce effects on directly affected operations jobs across all five use cases (transit agency types are not disaggregated in the table). The values in the table were the result of many assumptions, including but not limited to: • Transit vehicles and services can be successfully automated (both driving and non-driving functions). • Transit agencies and passengers will accept automated transit services. • Agency types (rural, small urban, and large urban) will have different adoption likelihoods. In addition, the estimated workforce effects of electrification were not included, and esti- mated workforce effects were calculated as if they happened immediately (estimates did not include attrition or the time-based effects of slow rollouts). In addition to the quantified estimated workforce effects, key findings included the following: • The degree and timing of potential workforce effects from transit vehicle automation were highly uncertain and were driven by multiple factors, including the degree of adoption, the services in which automated vehicles were used, and the operational models applied. • Potential job count and job description changes for bus operators (and the supervisory and training staff who work with them) will be driven largely by whether a human is kept on board every automated vehicle. • In general, transit vehicle automation has the potential to increase both the number of jobs in maintenance positions and the qualifications and technical expertise required for maintenance personnel. • Automated local bus service (Use Case #5) has the potential to affect the largest number of transit workers regardless of operational model and adoption scenario. Automated BRT resulted in the smallest potential estimated effect. • There were limited data available to provide the precise counts and characteristics of specific transit jobs beyond transit operators. • Transit industry professionals saw the potential benefits of transit vehicle automation but were uncertain about implementing automated transit vehicles and recognized the critical role that current bus operators play. • Front-line transit employees (e.g., operators and mechanics) had significant concerns about transit vehicle automation and were highly skeptical about potential benefits. This report also contains a discussion of suggested guiding principles and strategies that could help prepare the transit workforce for increases in automated transit vehicle deploy- ments. There is still much uncertainty about transit vehicle automation in general and work- force effects in particular. Although much uncertainty remains, the transit industry can begin by taking steps to prepare for automation and implement programs and activities that could help mitigate negative effects and maximize positive outcomes for the transit workforce.

4 The Impacts of Vehicle Automation on the Public Transportation Workforce Adoption Scenario, Operational Model, and Use Case Current Jobsa Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Partial Adoption Use Case #1: Bus Automation for Maintenance and Yard Operationsf 223,100 1,960 0.9% 97,160 127,900 Remote Operations Use Case #2: Low- Speed Automated Shuttles 159,600 (1,230) -0.8% 1,475 156,895 Use Case #3: Automated BRT 1,000 (215) -21.5% 280 505 Use Case #4: Automated MOD 63,710 (1,815) -2.8% 1,710 60,185 Use Case #5: Automated Local Bus Service 159,600 (25,370) -15.9% 30,030 104,200 In-Person Operations Use Case #2: Low- Speed Automated Shuttles 159,600 137 0.1% 2,842 156,895 Use Case #3: Automated BRT 1,000 29 2.9% 524 505 Use Case #4: Automated MOD 63,710 132 0.2% 3,657 60,185 Use Case #5: Automated Local Bus Service 159,600 3,905 2.4% 59,305 104,200 Full Adoption Use Case #1: Bus Automation for Maintenance and Yard Operationsf 223,100 4,460 2.0% 227,560 0 Remote Operations Use Case #2: Low- Speed Automated Shuttles 159,600 (2,565) -1.6% 2,935 154,100 Use Case #3: Automated BRT 1,000 (424) -42.4% 576 0 Use Case #4: Automated MOD 63,710 (9,385) -14.7% 8,535 45,790 Use Case #5: Automated Local Bus Service 159,600 (71,870) -45.0% 87,730 0 In-Person Operations Use Case #2: Low- Speed Automated Shuttles 159,600 253 0.2% 5,753 154,100 Use Case #3: Automated BRT 1,000 60 6.0% 1,060 0 Use Case #4: Automated MOD 63,710 804 1.3% 18,724 45,790 Use Case #5: Automated Local Bus Service 159,600 10,275 6.4% 169,875 0 Note: Values in the table are FTEs. They are in rounded terms and do not account for phasing, attrition, or service growth. aCurrent Jobs were the current number of FTEs in the directly affected job(s) working under bus transit modes to which the use case applied. Current Jobs were estimated by TTI (see Section 4.1.2). bJobs Gained (Lost) were the estimated number of FTEs that would be gained (or lost) as a result of adoption of the automated transit use case. c(%) Change in Jobs were calculated as the Jobs Gained (or Lost) ÷ Current Jobs. dRemaining Affected Jobs were the jobs that would remain at transit agencies that adopted the use case. These remaining affected jobs would have a change in their job tasks and would possibly require training. eUnaffected Jobs were the jobs that would not be affected by the adoption of the use case. These jobs would not have a change in their job tasks. fUse Case #1 is on its own line because it does not have the potential for both remote and in-person operations. Table 2. All use cases: estimated workforce effects on the directly affected operations jobs.