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65  C H A P T E R 7 This chapter presents the results of the study, including both the quantitative estimates and qualitative descriptions of the workforce effects of transit service automation. As previously discussed, the research team estimated the workforce effects for five different transit automation use cases using a workforce effect calculator, which was built for this study. The calculator esti- mated workforce effects on each directly affected job across all use cases, agency types, adoption scenarios, and operational models. (See Chapter 6 for a description of how the workforce effect calculator works.) The two adoption scenarios were: ⢠Partial adoption: The partial adoption scenario assumed that the implementation of differ- ent automated transit use cases at different transit agency types (i.e., rural, small urban, and large urban) would be limited. Partial adoption was somewhere between no adoption and full adoption, and the assumed levels of adoption used in this study were based on a combination of industry feedback, panel feedback, and professional judgment. ⢠Full adoption: The full adoption scenario was that in which all transit agencies would imple- ment an automated transit use case to fully replace conventionally driven transit services (assuming they have conventionally driven transit services to which the automated transit service could be applied). The full adoption scenario provided an estimate of the maximum potential workforce effects of automated transit services. The two operational models were: ⢠In-person operations: The in-person operations model assumed that there was a human on board every automated transit vehicle, and the onboard person was not dedicated to driving the vehicle. Because the person on board was no longer operating the vehicle, the research team referred to this position as an attendant. ⢠Remote operations: The remote operations model assumed that humans must monitor (and potentially control) automated transit vehicles but do so remotely and also assumed that humans monitor multiple vehicles at the same time. In the remote operations model, a person, referred to as a remote operator, would be housed in a control-center-like location with high- speed wireless connectivity with each AV to allow real-time video and audio transmission, communication, and control of automated transit vehicles. More details about both adoption scenarios and operational models are provided in Chapter 6. The tables in this section display effects per use case, adoption scenario, and operational model (and do not display results by agency type). A full breakdown of the estimated workforce effects by agency type can be found in Attachment 9. However, before examining and discussing the workforce effects on individual positions within specific use cases, this chapter first presents the potential total use case workforce effects on directly affected operations jobs. (Position-specific results begin in Section 7.2.) Results: Transit Automation Workforce Effects
66 The Impacts of Vehicle Automation on the Public Transportation Workforce 7.1 Summary of Use Case Workforce Effects Table 19 displays, for each use case, the total estimated effects of transit vehicle automation on the five directly affected operations jobs under the partial adoption and full adoption scenarios. Each table in this chapter displays the workforce effect calculatorâs four outputs (defined in Section 6.3) as well as an estimated percentage change in the number of jobs. (The percentage change in jobs was calculated as the Job Gain (Loss) output divided by the Current Jobs.) As a reminder, all estimated effects were calculated as if they happened immediately. The effects did not account for natural attrition that might have occurred and did not factor in the gradual phasing of the use case. As expected, the most significant impacts occurred in the remote operations model, in which many job duties were significantly restructured. Although there were differences across use cases, the results suggested that the largest potential for job loss was remote operations of Use Case #5: Automated Local Bus Service. In this scenario, estimated job losses ranged from 25,370 (partial 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 159,600 (1,230) -0.8% 1,475 156,895 1,000 (215) -21.5% 280 505 63,710 (1,815) -2.8% 1,710 60,185 159,600 (25,370) -15.9% 30,030 104,200 In-Person Operations 159,600 137 0.1% 2,842 156,895 1,000 29 2.9% 524 505 63,710 132 0.2% 3,657 60,185 Use Case #2: Low-Speed Automated Shuttles Use Case #3: Automated BRT Use Case #4: Automated MOD Use Case #5: Automated Local Bus Service Use Case #2: Low-Speed Automated Shuttles Use Case #3: Automated BRT Use Case #4: Automated MOD 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 159,600 (2,565) -1.6% 2,935 154,100 1,000 (424) -42.4% 576 0 63,710 (9,385) -14.7% 8,535 45,790 159,600 (71,870) -45.0% 87,730 0 In-Person Operations 159,600 253 0.2% 5,753 154,100 1,000 60 6.0% 1,060 0 63,710 804 1.3% 18,724 45,790 Use Case #2: Low-Speed Automated Shuttles Use Case #3: Automated BRT Use Case #4: Automated MOD Use Case #5: Automated Local Bus Service Use Case #2: Low-Speed Automated Shuttles Use Case #3: Automated BRT Use Case #4: Automated MOD Use Case #5: Automated Local Bus Service 159,600 10,275 6.4% 169,875 0 Note: Values in the table are FTEs. They have been rounded 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) caused by adoption of the automated transit use case. cPercent (%) 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 19. All use cases: estimated workforce effects on the directly affected operations jobs.
Results: Transit Automation Workforce Effects 67  adoption) to 71,870 (full adoption). The largest potential for job gain was also Use Case #5, but in the in-person operational model: estimated job gains ranged from 3,805 to 10,275. (However, readers should remember that Use Case #5 is also probably the furthest away from being ready for implementation.) The reasons for these substantial estimated losses and gains were due to many factors, including adoption ratios and task impact ratios affecting particular jobs. More detailed results for each use case are contained later in this chapter, starting with Section 7.2. 7.2 Use Case #1: Bus Automation for Maintenance and Yard Operations In Use Case #1, the most significant workforce effects would fall on mechanics and service persons (see Table 20). The results suggested that mechanic jobs may increase due to the increased time required to inspect and repair automation-supporting technologies. The mechanics would not only need additional time but also require additional training and technical expertise to successfully perform their maintenance tasks. The results suggested that service person jobs may decrease due to a significant decrease in the amount of time needed to maneuver buses in the yard between parking, fueling, washing, and other bus-readying stations. Operators, dispatchers, and supervisors were unaffected in any appreciable way because this use case focused on yard movements within O&M facilities and did not affect revenue service. The potential time savings for operators that resulted from not having to retrieve oneâs bus before pull-out or to park oneâs bus at pull-in was not substantial enough to justify reductions in labor hours. Instead, the research team assumed that the time savings would be reinvested back into revenue service to create a net impact of no change. 7.3 Use Case #2: Low-Speed Automated Shuttles In Use Case #2, the estimated workforce effects vary significantly across operational models and adoption scenarios (see Table 21). However, overall, the estimated impacts of this use case were relatively small when compared to other use cases. The low-level impact was due to the fact that the low-speed automated shuttle use case was only applied to fixed-route circulator Position Current Jobsa Partial Adoption Full Adoption Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Operator 170,000 0 0.0% 72,000 98,000 0 0.0% 170,000 0 Dispatcher 9,700 0 0.0% 3,800 5,900 0 0.0% 9,700 0 Supervisor 5,400 0 0.0% 2,200 3,200 0 0.0% 5,400 0 Mechanic 25,000 2,400 9.6% 13,400 14,000 5,400 21.6% 30,400 0 Service Person 13,000 (440) -3.4% 5,760 6,800 (940) -7.2% 12,060 0 Note: Values in the table are FTEs. They have been rounded 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) caused by adoption of the automated transit use case. cPercent (%) 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. Table 20. Use Case #1: estimated workforce effects on the directly affected operations jobs.
68 The Impacts of Vehicle Automation on the Public Transportation Workforce or feeder services, which make up a relatively small portion of all fixed-route bus service. Even under a full adoption of the remote operations model (where all fixed-route circulator or feeder services are replaced by remotely operated low-speed automated shuttles), the research team estimated a potential job loss of 3,100 fixed-route operators (2.6 percent out of 120,000). Under the in-person operational model, operator job displacement would be zero; however, current operators would become shuttle attendants. The effects on dispatchers and supervisors were small, with all job gains or losses falling below 1 percent. Mechanics would experience similar impacts as with other use cases, potentially seeing some increase in jobs caused by the increased amount of time needed to maintain automation-supporting technology on the vehicles. (Although smaller vehicles than traditional, full-sized buses, the research team assumed maintenance time would increase slightly.) The estimated impact on ser- vice persons depended on the operational model. Because low-speed automated shuttles were significantly smaller than full-sized buses, the research team assumed that the amount of time spent cleaning, fueling, and preparing the vehicles for service would be reduced. With in-person operations, service person jobs reduced slightly from this change. However, with remote opera- tions, service persons would perform the daily pre- and post-trip inspections, potentially causing a slight increase in the number of jobs needed, even after accounting for reduced servicing time. 7.4 Use Case #3: Automated BRT In Use Case #3, the percentage changes were large; however, this was caused by starting with a relatively small number of current jobs (about 1,000) engaged in providing BRT service (see Table 22). Because the research team assumed that this use case would be very attractive to cur- rent BRT-providing transit agencies, a significant number of conventionally driven BRT services would be replaced by automated BRT servicesâeven under the partial adoption scenario. The Remote Operations Operational Model and Fixed-Route Bus Position Current Jobsa Partial Adoption Full Adoption Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Operator 120,000 (1,500) -1.3% 500 118,000 (3,100) -2.6% 1,000 115,900 Dispatcher 4,300 (15) -0.3% 55 4,230 (25) -0.6% 115 4,160 Supervisor 3,300 (5) -0.2% 50 3,245 (10) -0.3% 100 3,190 Mechanic 20,000 190 1.0% 550 19,640 370 1.9% 1,090 19,280 Service Person 12,000 100 0.8% 320 11,780 200 1.7% 630 11,570 In-Person Operations Operator 120,000 0 0.0% 2,000 118,000 0 0.0% 4,100 115,900 Dispatcher 4,300 (1) 0.0% 69 4,230 (2) 0.0% 138 4,160 Supervisor 3,300 (2) -0.1% 53 3,245 (5) -0.2% 105 3,190 Mechanic 20,000 190 1.0% 550 19,640 370 1.9% 1,090 19,280 Service Person 12,000 (50) -0.4% 170 11,780 (110) -0.9% 320 11,570 Note: Values in the tables are FTEs. They have been rounded 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) caused by adoption of the automated transit use case. cPercent (%) 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. Table 21. Use Case #2: estimated workforce effects on the directly affected operations jobs.
Results: Transit Automation Workforce Effects 69  patterns in the results were very similar to other use cases. Operator jobs would likely not be displaced with the in-person operations model; however, operators would become attendants. With the remote operations model, operator jobs would potentially be reduced. The impacts on dispatchers and supervisors would be relatively small. Mechanic jobs would potentially increase due to increased maintenance demands, and service person jobs would potentially increaseâ especially with the remote operations model, in which service persons would be performing pre- and post-trip inspections instead of operators. 7.5 Use Case #4: Automated MOD In Use Case #4, the potential reduction in operator jobs was relatively high when compared to other use cases (see Table 23). Under the partial adoption scenario remote operations model, operator jobs were estimated to decrease by 2,100 (4.1 percent of all demand-response opera- tors). Part of the reason the potential job reduction was a larger number is that the research team assumed this use case would be implemented at rural transit agencies more than other use cases due to the high proportion of rural agencies that operate general public demand-responsive service (non-ADA paratransit). Like other use cases, the estimated workforce effects were more significant with the remote operations model; maintenance jobs (mechanics and service persons) were estimated to increase or stay relatively flat. 7.6 Use Case #5: Automated Local Bus Service Use Case #5 had the most dramatic and large-scale potential workforce effects when compared to the other use cases because Use Case #5 involved replacing a significant portion of current transit service (which is mostly local bus service) with automated local bus transit (see Table 24). Operational Model and BRT Position Current Jobsa Partial Adoption Full Adoption Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Remote Operations Operator 670 (260) -38.8% 70 340 (520) -77.6% 150 0 Dispatcher 85 (8) -9.4% 37 40 (15) -17.6% 70 0 Supervisor 50 (2) -4.0% 23 25 (4) -8.0% 46 0 Mechanic 120 30 25.0% 90 60 60 50.0% 180 0 Service Person 75 25 33.3% 60 40 55 73.3% 130 0 In-Person Operations Operator 670 0 0.0% 330 340 0 0.0% 670 0 Dispatcher 85 (1) -1.2% 44 40 (1) -1.2% 84 0 Supervisor 50 (1) -2.0% 24 25 (2) -4.0% 48 0 Mechanic 120 30 25.0% 90 60 60 50.0% 180 0 Service Person 75 1 1.3% 36 40 3 4.0% 78 0 Note: Values in the table are FTEs. They have been rounded 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 (BRT only). 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) caused by adoption of the automated transit use case. cPercent (%) 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. Table 22. Use Case #3: estimated workforce effects on the directly affected operations jobs.
70 The Impacts of Vehicle Automation on the Public Transportation Workforce Operational Model and Demand-Response Position Current Jobsa Partial Adoption Full Adoption Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Remote Operations Operator 51,000 (2,100) -4.1% 700 48,200 (11,000) -21.6% 3,000 37,000 Dispatcher 5,400 (55) -1.0% 245 5,100 (280) -5.2% 1,320 3,800 Supervisor 2,100 (10) -0.5% 110 1,980 (55) -2.6% 555 1,490 Mechanic 4,300 140 3.3% 410 4,030 850 19.8% 2,450 2,700 Service Person 910 210 23.1% 245 875 1,100 120.9% 1,210 800 In-Person Operations Operator 51,000 0 0.0% 2,800 48,200 0 0.0% 14,000 37,000 Dispatcher 5,400 (4) -0.1% 296 5,100 (25) -0.5% 1,575 3,800 Supervisor 2,100 (5) -0.2% 115 1,980 (25) -1.2% 585 1,490 Mechanic 4,300 140 3.3% 410 4,030 850 19.8% 2,450 2,700 Service Person 910 1 0.1% 36 875 4 0.4% 114 800 Note: Values in the table are FTEs. They have been rounded 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 (demand response only). 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) caused by adoption of the automated transit use case. cPercent (%) 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. Table 23. Use Case #4: estimated workforce effects on the directly affected operations jobs. Operational Model and Fixed-Route Bus Position Current Jobsa Partial Adoption Full Adoption Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Jobs Gained (Lost)b % Change in Jobsc Remaining Affected Jobsd Unaffected Jobse Remote Operations Operator 120,000 (32,000) -26.7% 9,000 79,000 (90,000) -75.0% 30,000 0 Dispatcher 4,300 (260) -6.0% 1,240 2,800 (760) -17.7% 3,540 0 Supervisor 3,300 (110) -3.3% 1,090 2,100 (310) -9.4% 2,990 0 Mechanic 20,000 3,800 19.0% 11,100 12,700 10,000 50.0% 30,000 0 Service Person 12,000 3,200 26.7% 7,600 7,600 9,200 76.7% 21,200 0 In-Person Operations Operator 120,000 0 0.0% 41,000 79,000 0 0.0% 120,000 0 Dispatcher 4,300 (20) -0.5% 1,480 2,800 (65) -1.5% 4,235 0 Supervisor 3,300 (45) -1.4% 1,155 2,100 (130) -3.9% 3,170 0 Mechanic 20,000 3,800 19.0% 11,100 12,700 10,000 50.0% 30,000 0 Service Person 12,000 170 1.4% 4,570 7,600 470 3.9% 12,470 0 Note: Values in the table are FTEs. They have been rounded 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 (fixed-route bus, excluding BRT). 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) caused by adoption of the automated transit use case. cPercent (%) 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. Table 24. Use Case #5: estimated impacts on the directly affected operations jobs.
Results: Transit Automation Workforce Effects 71  With the remote operations model, the results suggested a potential reduction in operator jobs of between 32,000 and 90,000 for partial and full adoption, respectively. Mechanics jobs could potentially increase by between 3,200 and 9,200 for partial and full adoption, respectively. With the in-person operations model, operator jobs were left unchanged, dispatchers and supervisors experienced marginal changes, and maintenance personnel experienced increases in jobs. 7.7 Discussion of Effects on Other Transit Jobs and Organizational Structures This section provides some ideas about the potential workforce effects on transit jobs that are not the five directly affected operations jobs. 7.7.1 Changes to Indirectly Affected Key Jobs As previously discussed, indirectly affected key jobs are those front- and second-line and supervisory transit agency jobs that work directly with operators, transit planning and schedul- ing, and vehicle and yard O&M. These jobs will be indirectly affected due to their proximity to directly affected operations jobs. The research team did not include indirectly affected key jobs in the workforce effect calcula- tor or collect data about the potential impact on these jobs. However, the concepts of opera- tions produced by the research team combined with the estimated workforce effects on directly affected jobs provided some ideas about how indirectly affected key jobs may be changed. Some potential changes are described as follows: ⢠Bus garage superintendent: A bus garage superintendentâs job is to manage the people and vehicles used to provide daily transit service. With the implementation of automated transit services, that job may be funda- mentally altered. With the in-person operations model, there may not be much that changes from the current job except that operators will be attendants, and the skills and tasks performed by attendants will be different, which may have trickle-down effects on what bus garage super- intendents focus on when managing attendants, what attendant behaviors are reinforced or disciplined, and even what bus garage superintendent skills are highly valued. With the remote operations model, superintendents may have a significantly reduced role, or their role may focus more on making sure that vehicles are prepared and ready for daily service. ⢠Bus operations trainer: An operations trainerâs main job is to train both new and current bus operators. With the implementation of automated transit services, trainers might have a very different job than they do today. Today, a significant portion of operator training focuses on driving (i.e., oper- ating a bus). This driving training may also include assistance and testing for operators to earn their commercial driver licenses. With both the remote and in-person operations model, training for actual hands-on vehicle driving may be reduced. With remote operations, remote operators will need to learn how to use their specialized remote operations equipment to remotely control vehicles. With in-person operations, attendants will still need to know how to drive the vehicle (in case it is needed); however, because the attendantâs job will be mainly focused on customer service, training may be almost completely focused on customer ser- vice skills, providing passenger assistance, and other non-driving tasks. The number of bus operations trainers needed is largely a function of the number of bus operators that need to be trained and the number of hours required to deliver successful training. If there are sub- stantial shifts in the number of operator jobs or the training hours grow or shrink compared to current training hours, then the number of bus operations trainer jobs may also change.
72 The Impacts of Vehicle Automation on the Public Transportation Workforce ⢠Maintenance trainer: A maintenance trainerâs main job is to train both new and current bus mechanics and technicians (referred to as mechanics). With the implementation of automated transit ser- vices, maintenance trainers may need to obtain a whole new set of skills of their own to be able to effectively train mechanics to inspect and maintain automated transit vehicles. Auto- mated transit vehicles will come with at least the baseline supporting technologies discussed in Section 2.2 and may also have additional supporting technologies specifically needed for transit automation (see Section 2.3). As discussed previously, the estimated workforce effects on mechanics, in general, were an increase in the amount of time needed to perform main- tenance activities, which resulted in a potential net increase in mechanic jobs. Increasing mechanic jobs and a potential increase in the time required to deliver mechanic training may also result in an increased need for maintenance trainer jobs. ⢠Parts clerk: A parts clerkâs main job is to inventory, manage, and distribute bus parts to mechanics. With the implementation of automated transit services, the job of a parts clerk may also be impacted by the increasing number and complexity of vehicle parts that must be maintained in inventory. There are many parts on AVs that are currently not commonplace in the parts storerooms of today, and parts clerks will be expected to become familiar with these compo- nents, including how to safely secure and store them, any special handling requirements, and also what the restocking lead times are to ensure that supplies do not run out, causing AVs to be down, waiting for parts. Apart from the added knowledge of managing the new parts of automated transit vehicles, the research team does not anticipate any other notable changes for the parts clerk position. ⢠O&M facilities maintainer: An O&M facilities maintainerâs main job is to maintain bus O&M facilities, including maintaining electrical, HVAC, plumbing, and communications systems, as well as buildings and yard infrastructure. Transit automation-related impacts on this position may vary, depending on whether yard and maintenance movements are automated (i.e., Use Case #1) or not. If automated yard movements are implemented, additional technology infra- structure (e.g., automation-supporting V2I components) may be added to O&M facilities, increasing the workload on facilities maintainers and requiring them to gain additional tech- nical expertise to install, inspect, and maintain that automation-supporting infrastructure. As discussed in Use Case #1, to take full advantage of automated yard movements, other non-bus systems (e.g., bus washers, fueling islands, etc.) could also eventually be auto- mated, further increasing the presence of complex technology at O&M facilities and the workload of facilities maintainers. Even without automated yard movements, if there are automated transit vehicles in the fleet, those vehicles may rely heavily on in-yard wire- less communications to ensure that AVs are receiving the latest software updates and are transferring their potentially large amounts of collected data for processing and analysis. Ongoing inspection and maintenance of this critical infrastructure may be added to the facility maintainerâs already long list of duties. The additional workload created and skills needed may result in a net increase in facility maintainer jobs and may also breed increasing specialization. ⢠Short-range transit planner/scheduler: A short-range transit plannerâs main job is to prepare, evaluate, and analyze short-range (1- to 5-year) transit plans for fixed-route bus services. A schedule makerâs main job is to prepare transit schedules (trips, blocks, runs, and rosters) for transit services. Although these two jobs may involve different skill sets and have historically been separated at larger transit agencies, there was some evidence that smaller transit agencies have combined the positions and even some larger agencies were moving toward combined planner-scheduler positions (see Walk et al. 2019). In general, both the planning and scheduling components
Results: Transit Automation Workforce Effects 73  of the planner-scheduler job will be impacted by automated transit services, but the impacts vary with the use case and with the operational model. With in-person operations, a planner- schedulerâs job likely would not experience significant change. Although operators are now attendants, scheduled trips would still need to be blocked and attendant runs would still need to be createdâincorporating such typical scheduling inputs as lunch breaks, maximum run lengths, reliefs, etc. For planning activities, service standards may remain the same, given that the cost profile (and therefore productivity requirements) of automated transit services would potentially look much as it does today. With remote operations, a planner- schedulerâs work may actually look quite different. Planner-schedulers may still need to create remote operatorsâ runsâensuring that each vehicle block is part of a group assigned to a remote operatorâs caseload. However, given that operators are not in the field on board vehicles, the complexities of lunch breaks and street reliefs are reduced. Vehicle trips and blocks (at least for fixed-route services) would likely continue to be produced to guide the level of service provided to meet passenger demand. However, with remote operations, service standards might change to allow for increased, policy-driven service levels that permit less productive services to continue to operate due to the potentially lower cost profile of remotely operated automated transit services. Regardless of the operational model, planner- schedulers will likely have an increasingly abundant amount of data to work with from automated transit vehicles. In addition to increasingly detailed traditional transit data (e.g., running times, dwell times, and passenger loads), automated transit vehicles could provide new data streams to schedulers that prove useful in the planning and scheduling process. For example, AVs could provide data to analyze specific causes of en route delays (e.g., bus- to-bus interference, signal queuing), allowing planner-schedulers to more proactively work to address speed and reliability challenges without requiring additional field data collection. Planner-schedulers will need increasing analytical tools and skills to use those data effec- tively. However, unless the implementation of automation results in an increased amount of transit service, the research team believes that job gains or losses in planner-scheduler jobs are unlikely. ⢠Transit safety and security personnel: Transit safety and security personnel include sworn police officers working for the transit agency or for local/state governments and civilian security personnel (e.g., security guards). The workforce effects of automated transit services on safety and security personnel would likely vary, depending on the operational model. With both operational models, the main impact was that safety and security personnel would need training to understand how to work with automated transit vehicles (e.g., how to safely shut them down and how to disengage automated systems and manually control them) and how to provide physical security and cybersecurity to AVs. A full discussion of AV cybersecurity was beyond the scope of this report; however, it will almost certainly be an important added responsibil- ity for safety and security personnel. With in-person operations, the workforce effects on safety and security personnel would potentially be marginal. There are still attendants on board every vehicle, and safety and security personnel would probably work with atten- dants, dispatchers, and supervisors much like they do today. With remote operations, safety and security personnel may have an increased roleâmuch as the research team anticipated an increased role of supervisors. Because there are no dedicated transit agency employees physically present on the transit vehicle, both random and targeted safety and security checks on transit vehicles may be increased. Although uncertain, some transit industry representatives reported concerns of a potential increase in onboard incidents if transit vehicles were not to have an attendant. Remote operators may issue requests for service more frequentlyâasking safety and security personnel to handle onboard inci- dents, enforce fare collections, or perform other duties. If the need for safety and security personnel increases, transit agencies may need to create their own transit-dedicated safety
74 The Impacts of Vehicle Automation on the Public Transportation Workforce and security teams (many transit agencies do not have their own police force) or may increase their use of contracted security services. 7.7.2 General Potential Effects on Transit Staffing and Organizational Structures This subsection briefly summarizes some potential effects on broad transit staffing strategies and organizational structures. As a reminder, this subsection is a description of possibilitiesâ not certaintiesâand was based on both feedback received during industry engagement and the research teamâs professional judgments. Potential impacts include: ⢠Potential C-suite changes: Transit vehicle automation is a new and specialized area in the transit industry that requires a unique combination of technological and transit operational expertise. Who will oversee the implementation and operation of automated transit services? The responsibility could fall to one of the current traditional leadership rolesâlike a chief information officer or technol- ogy officer. Or the responsibility could be given to existing roles that focus on innovation (e.g., a chief innovation officer). If transit vehicle automation is an important part of a transit agencyâs strategy and the work is significant enough, transit agencies might explore creating a new C-suite roleâfor example, a chief automation officer or director of transit automation, responsible for automation expansion strategy and implementation. Such a role might also be responsible for anticipating and preparing transit agency employees for the impacts of tran- sit automation by leading multi-departmental initiatives with human resources, information technology, operations, maintenance, and others. ⢠New departments: In addition to new, specialized leadership, implementing and operating automated transit systems could require the creation of specialized departmentsâagain, especially at larger transit agencies or private providers with significant automation plans. This department, perhaps called the Office of Automation or the Bus Automation and Operations Department, could be responsible for all things automation, managing the business processes and technologies sup- porting transit automation. However, given that automated transit service is still very much an operational, not just a technological, challenge, an automation-focused department or group may need to be firmly grounded in transit operationsâperhaps by being under the purview of a chief operations officer or through very strong standard operational processes that clearly define the roles and responsibilities of the automation department and other groups in the transit agency. ⢠Personnel specialization: Generally speaking, the research team took the approach that transit agencies would upskill their current workforce to be trained and certified for automated transit O&M. This training may create a new, specialized workforce working in parallel with employees working with conventionally driven transit services. Specialized employees could be needed in all of the directly affected operations jobs as well as other transit jobs. Working with automated transit vehicles and services could be used as a promotional opportunity for employeesâmade first available to any employees whose jobs are impacted by the implementation of automated transit services. Examples of potential job titles include: â Remote operator or AV attendant. â AV dispatcher. â AV supervisor. â AV technician. â Automated transit service planner-scheduler. ⢠Automation cooperatives: Especially with smaller transit agencies, the costs and risks of deploying automated transit services may be too high; however, if transit agencies join forces and participate in
Results: Transit Automation Workforce Effects 75Â Â larger multicity, statewide, or multistate automation cooperatives (e.g., the Automated Bus Consortium), the cost-benefit calculus could shift, enabling smaller agencies to imple- ment automated transit services. Participating agencies could procure vehicles and equip- ment together; cooperate in testing, implementation, and operations; and share resources and lessons learned. In summary, the research team and the transit industry still have a lot to learn and understand about how transit agencies will prepare for, implement, and operate automated transit services in the long run. There are many potential paths forward, which will be sculpted by the decisions that transit agencies make today and over the next several years.
