Abstract
Public transportation is critical infrastructure serving millions of people across the United States. With roughly 4 billion individual trips occurring annually, it is the primary mode of transportation for many commuting to and from work, school, and leisure activities. Over the past several years, there has been an increase in investment in automated vehicle (AV) technology for buses. The use of AV technology has the potential to fundamentally impact public transit operations. While there are ambitious plans for automated bus deployments across the country, operating transit is more complex than light-duty passenger vehicles. Buses, for example, are significantly larger and operate in highly variable environments near vulnerable road users. Even in the case of smaller vehicles such as vans, there are still many technical challenges to overcome to navigate these complex environments safely. Furthermore, transit operations require supporting passengers and maintaining safety inside the vehicle. Due to both technical and operational challenges, transit vehicles, including buses and vans, will continue to require skilled human operators, even as automated vehicle capabilities are incorporated. Introducing new technology will impact operator’s duties and actions, as well as passenger safety and experience.
To help maintain transit’s high level of safety for passengers, it is essential to understand how automation stands to affect the roles and day-to-day tasks of trained operators. Driver assistance automation, such as pedestrian warnings and lane-centering, can potentially improve the safety and workload of trained operators. At the same time, automation can create new kinds of safety issues caused by the interactions in human-autonomy teams and can intensify work as people primarily take over from automation in the most challenging situations. It is crucial to consider the safety of incorporating automation technologies into fleets and to develop training for operators to work effectively with such technologies. Our research first examines how autonomous vehicle technologies could impact transit operations, and specifically the jobs of transit workers. We will then collaborate with transit drivers to understand the kinds of advanced driver-assistance systems (ADAS) and interfaces that would help them in their work and improve transit operations.
Through a participatory design approach, this project will examine past and ongoing transformations of transit infrastructure in order to envision the future of transit with operators. Centering safety and equity of the socio-technical infrastructure of transit, this project aims to develop novel, operator-driven systems that could further enhance operations. This is expected to yield (1) empirical findings on the forms of autonomy (current and proposed) drivers perceive as being helpful to their work, as well as necessary components of implementation (e.g., training, human-machine interfaces), (2) methodological insights on co-design strategies for generating novel directions for ADAS within transit, as well as accounting for the potential, unintended harms of autonomy, and (3) theoretical findings that contribute core understandings on autonomous systems impacts to safely and workload.
Description
Timeline
Strategic Description / RD&T
Our project aligns with the US DOT Research, Development and Technology Strategic Plan in several ways: advancing two of its strategic goals and explicitly motivated by one of its primary innovation principles.
Goal 1: Safety — Bus operations are quite safe compared to personal vehicle operations, with an average of 40 occupant fatalities per year (as opposed to 23,000 per year in light-duty passenger vehicles) (Federal Transit Administration 2021). Nevertheless, there are still opportunities to incorporate novel active safety technologies to enhance driving. Our focus on developing operator-driven ADAS systems is rooted in the idea that it is necessary to address transit-specific safety needs (e.g., docking assist and knock-down mitigation) such that riders—as well as pedestrians and cyclists traveling alongside transit vehicles—continue to travel without incident.
Goal 2: Equity — Public transit fosters accessibility by offering equal and affordable transportation options, particularly benefiting those with disabilities, low-income individuals, and elder populations. It reduces congestion, lowers environmental impact, and expands economic opportunities while promoting social inclusion and community engagement. Our work seeks to enhance equity by exploring strategies for return communication to support passengers with disabilities in transit service. This focus is motivated by preliminary work that has found operators desire advanced information about the needs of elderly passengers and people with disabilities who require assistance.
Innovation Principle: Support workers — Currently, transit operations labor is largely performed by workers who have few opportunities for their knowledge to be meaningfully integrated into the design and implementation of automated systems. Building on the legacies of participatory design, our research seeks to give transit workers a seat at the table in shaping innovation aimed to leverage the benefits of new technology to enhance safety and preserve fulfilling aspects of work.
Deployment Plan
Autonomous driving technologies are currently being developed and piloted for use in transit. Though there is enthusiasm for the potential of this technology to improve operations and safety, current and near-future systems will be focused primarily on driver assistance, meaning such improvements will only be realized through effective human-machine teaming. Today, there are two key issues with how ADAS technologies are being developed for the transit industry:
Systems are being designed without input from operators. Emergent technologies within the space of AVs are not designed for the needs of transit operators.
While AV systems are being extensively tested in the context of private passenger cars and vans with non-professional drivers, there has been little research on the needs of transit operators and where autonomy could help. From our preliminary research, operators have not been included in the design of new systems. This lack of input can lead to unforeseen issues at the time of implementation. There is also the possibility that opportunities for low-cost, readily implementable ADAS systems that could improve drivers’ work may be overlooked.
We do not fully understand how such human-machine interaction (HMI) changes will impact driving operations, including safety and work intensity.
Prior research on the introduction of automation into aviation shows that while automation can prevent many common issues and accidents, the added complexity of automated systems creates new kinds of accidents. We are already seeing these kinds of new accidents today, such as an incident in Las Vegas where a truck backed into an AV, and the trained safety driver could not back out of the way due to having limited control of the vehicle. Improvements in aviation safety have come from developing an understanding of and designing for human-machine interaction. Without considering the human-machine team, people’s work may be intensified and potentially become harder, and the real value of automated technologies will not be realized.
Based on these two problems, we see opportunities for future research focused on developing advanced driver assistance systems with transit operators.
Understanding the Opportunities (and Potential Harms) of ADAS in Transit
In our preliminary work, bus operators consistently expressed interest in driver assistance technologies that they had experienced in other contexts such as their own personal vehicles, including lane centering and parking assist. Building on these findings, we see an opportunity to develop novel, operator-driven systems that could further enhance safety, particularly as it relates to vulnerable road users (pedestrian awareness) and low-speed, but challenging docking maneuvers.
Research questions:
What forms of autonomy (existing and proposed) do drivers perceive as being helpful to their work?
What potential harms or oversights do drivers identify in the implementation of future autonomous systems?
Phase 1 (Q4 2023/Q1 2024): Large-scale survey of operators examining perceptions of existing ADAS technologies, as well as identifying opportunities for new development and responsible implementation.
To begin to address these questions on appropriate forms of driver assistance within a transit context, we will develop a large-scale survey of operators meant to examine perceptions of existing ADAS technologies as well as identify opportunities for new development. In developing the instrument, we draw from scholarship in organizational psychology focused on offering insights into understanding how operators embrace and integrate technological innovations on three levels including operation, use, and work system (CITE - Technology Acceptance Model; Work Design Questionnaire) and on prior work around driver acceptance of ADAS systems in passenger vehicles (Penttinen & Luoma 2020). For example, survey items could include questions like: “The use of the driver assistance system increases my professional effectiveness” or “I feel the behavior of the driver assistance system is predictable.” (Likert scale from 1 = totally disagree to 5 = totally agree). We will deploy our survey to transit operators across the US and Canada via our multi-year partnership with the Transportation Workers Union (TWU), Amalgamated Transportation Union (ATU), and AFL-CIO Technology Institute.
Phase 2 (Q2 2024): Hazard analysis with operators of specific automation technologies that may be implemented in bus operations.
Building on the results of the survey, we will follow up with focus group discussions with a selection of respondents oriented around two key activities: 1) discussion of potential ADAS technologies that would be useful to operations and 2) hazard analysis of current and near-future systems already available. Our hazards analysis activity will engage operators in thinking through the potential issues that may arise when automation is used during transit operations. This will build upon prior hazard analyses on Level 1 and 2 automation in bus transit (Becker, Nasser & Brewer 2020) Our hazards discussions provide an opportunity to consider potential risks and to enumerate unique operating conditions and challenges (inclement weather issues, contextually specific challenges due to region) that may not be well documented, but are well-known by experienced operators. We will structure our activity using methods from Failure Modes and Effects Analysis and through a card game called “What Could Go Wrong?” developed by PI Martelaro (Martelaro and Ju 2020).
References —
Becker, Christopher, Ahmad Nasser, and John C. Brewer. Hazard and Safety Analysis of Automated Transit Bus Applications. No. FTA Report No. 0161. United States. Department of Transportation. Federal Transit Administration, 2020.
Gruchmann, Tim, Nadine Pratt, and Axel Salzmann. "Bus driver's technology acceptance for driving assistants." In Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 31, pp. 663-689. Berlin: epubli GmbH, 2021.
Johansson, Mikael, Fredrick Ekman, MariAnne Karlsson, Helena Strömberg, and Joakim Jonsson. "ADAS at work: Assessing professional bus drivers’ experience and acceptance of a narrow navigation system." Cognition, Technology & Work 24, no. 4 (2022): 625-639.
Martelaro, Nikolas, and Wendy Ju. "What could go wrong? Exploring the downsides of autonomous vehicles." In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 99-101. 2020.
Penttinen, Merja, and Juha Luoma. "Acceptance and use of ADAS." In 8th Transport Research Arena, TRA 2020-Conference cancelled, p. 67. Liikenne-ja viestintävirasto Traficom, 2020.
Silva, Daniel, and Liliana Cunha. "Aside from Deterministic Prophecies, What Is Missing in the Contemporary Debate on Automation and the Future of Work? The Case of Automated Vehicles." Social Sciences 11, no. 12 (2022): 566.
Expected Outcomes/Impacts
This project will distribute much-needed knowledge about the impacts of autonomy on the transit sector, critical infrastructure to millions of Americans. As a part of this research, we will (1) engage members of the transit workforce to assess existing or proposed driver assistance technologies according to how they do or do not meet their needs, (2) draw from our empirical work to author articles for academic publication (e.g., CHI or CSCW) and public-facing outlets (e.g., The Atlantic, Nature Cities), (3) produce a webinar translating our research into guidance for transit organizations on human-autonomy teaming, and (4) circulate findings with local and national policymakers to help inform the legislative debate around transportation policy.
Additionally, we will connect with the American Public Transportation Association, Advocates for Highway and Auto Safety, the Center for Advanced Automotive Research, and the Intelligent Transportation Society of America to share our insights. We may also connect with vehicle manufacturers; We have been in conversation with Beep, a company focusing on Mobility as a Service, and operating small AV transit pod operations around the US.
Expected Outputs
The research seeks to identify the types of autonomy that transit operators perceive as beneficial for their work, along with necessary components for successful implementation such as training and human-machine interfaces. Through a collaborative approach, the project will generate ideas for innovative systems that enhance transit operations based on the insights and needs of transit workers. Specifically, we will produce the following outputs:
Two academic papers about the perceptions of ADAS among transit operators. The first will draw from a large-scale survey of operators in North America. The second will report on findings from a series of collaborative hazard analysis activities.
A set of design proposals informed by our empirical research with operators. These proposals will be shared via an online webinar. We will invite transit organizations, policy analysts, and vehicle manufacturers, and host a live question and answer period. We will later share a recording of the presentation online.
A white paper translating our academic findings into actionable policy insights. This will be informed by our experience preparing and disseminating a 2022 policy report that was circulated throughout the Department of Transportation (Fox and Caldwell delivered an in-person briefing on this report in Fall 2022).
TRID
The scholarship most immediately relevant to our proposed research is a recent report from Walk et al., “The Impacts of Vehicle Automation on the Public Transportation Workforce,” which identifies a set of likely use cases for automation within the transit sector — analyzing the effects on the transit workforce and identifying strategies to prepare the workforce for and mitigate the negative impacts of automation. One of the use cases the authors identify is automated local bus transit, which they note as having the potential to affect the largest number of transit workers. An earlier DoT report to the US Congress entitled “Driving Automation Systems in Long-Haul Trucking and Bus Transit” describes the potential shifts of responsibilities that would be necessary to support transit vehicle automation, as well as quality of life and wage impacts for transit workers whose roles could be affected. The report also suggests that the non-driving tasks operators perform would be costly to automate or reassign.
Extending this initial work, we align our research with Walk et al.’s recommendation to take an employee-centric approach, using participatory methods to develop and assess novel, operator-driven systems that could further enhance operations.
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| sarahfox@cmu.edu |
Fox, Sarah |
Carnegie Mellon University |
PI |
Faculty - Untenured, Tenure Track |
| nikmart@cmu.edu |
Martelaro, Nikolas |
Carnegie Mellon University |
Co-PI |
Faculty - Untenured, Tenure Track |
Budget
Amount of UTC Funds Awarded
$98000.00
Total Project Budget (from all funding sources)
$100000.00
Documents
| Type |
Name |
Uploaded |
| Data Management Plan |
476_Co-designing_Safety-Enhancing_ADAS_with_Transit_Operators_DATA_MANAGEMENT_PLAN.docx |
Oct. 11, 2023, 2:06 p.m. |
| Publication |
“The bus is nothing without us”: Making Visible the Labor of Bus Operators Amid the Ongoing Push Towards Transit Automation |
March 31, 2024, 5:52 p.m. |
| Presentation |
How to Make Sense of Bus Transit Automation |
March 31, 2024, 5:52 p.m. |
| Presentation |
How to Make Sense of Bus Transit Automation |
March 31, 2024, 5:52 p.m. |
| Presentation |
(Re)Working AI |
March 31, 2024, 5:52 p.m. |
| Progress Report |
476_Progress_Report_2024-03-31 |
March 31, 2024, 5:52 p.m. |
Match Sources
No match sources!
Partners
| Name |
Type |
| AFL-CIO |
Deployment & Equity Partner Deployment & Equity Partner |
| Transport Workers Union |
Deployment & Equity Partner Deployment & Equity Partner |
| Amalgamated Transit Union |
Deployment & Equity Partner Deployment & Equity Partner |
