國立虎尾科技大學 |

AI Behind the Wheel: Work, Economics, and Preferences in the Era of Autonomous Vehicles /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	AI Behind the Wheel: Work, Economics, and Preferences in the Era of Autonomous Vehicles // Leah Kaplan.
作者:	Kaplan, Leah,
面頁冊數:	1 electronic resource (218 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
標題:	Transportation. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31556194
ISBN:	9798384025696

AI Behind the Wheel: Work, Economics, and Preferences in the Era of Autonomous Vehicles /
Kaplan, Leah,

AI Behind the Wheel: Work, Economics, and Preferences in the Era of Autonomous Vehicles /Leah Kaplan. - 1 electronic resource (218 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Autonomous vehicles (AVs) have the potential to dramatically disrupt transportation patterns and reshape cities and communities. At the core of this disruption is the substitution of a human driver with artificial intelligence (AI) technologies. Will AI-enabled vehicle automation help actualize visions of an enhanced transportation future, or will it exacerbate existing problems? The answer will ultimately depend on the capabilities of AVs and their position in our complex, sociotechnical transportation systems. While history and prior research offer lessons on how conventional automation can impact systems, less is known about the potential effects of AI-enabled automation, particularly when AI technologies are deployed to perform non-routine manual tasks. This dissertation unpacks several potential impacts of AVs and uses them as a case study of the potential societal effects of AI-enabled automation in the physical world.AI advancements are changing the ways in which automation technologies can interact with and displace human labor. Unlike routine manual tasks like repetitive assembly that have undergone substantial substitution by conventional automation technologies, non-routine physical tasks have historically had limited opportunities for substitution or complementarity. AI advancements, however, are expanding automation technologies' capabilities within the non-routine task realm, demanding research into their resulting labor impacts. The first study in this dissertation uses direct observations, semi-structured interviews, and archival data to identify and map how tasks and labor roles change in response to the introduction of autonomous vehicles to a taxi work system. This detailed analysis empirically demonstrates that introduction of AI technologies spurs the rebundling of tasks and reorganization of adjacent labor roles, and reveals three archetypal patterns of role change: distributing, consolidating, and scaffolding. These patterns can be used to refine labor outcome analyses and predict labor impacts at the task, role, and sector or economy level.What motivates the substitution of a human driver with an AV system? While the promise of safer road travel is an oft cited motivation, the lure of lower operating costs is arguably the primary driver of investment in AV development. For years, Transportation Network Companies (TNCs) like Uber and Lyft, and emerging AV companies like Waymo, Cruise, and Zoox, have raised billions of dollars by promising a future of autonomous taxi fleets (robotaxis) that would eliminate the cost of a driver and pave a path to profitability. The future operating costs of robotaxi services, however, remain highly uncertain. Prior studies estimating the future costs of robotaxi services lack precision in two key areas: 1) the cost of autonomous vehicle technologies and 2) the labor costs needed to operate a robotaxi service. These two cost categories are critical as the payoff to firms for automating a particular task depends on the cost of the technology relative to the cost of the worker who performs that task.The second study in this dissertation draws on the first study's findings about changing labor roles to develop ground-up cost models for a traditional taxi and a robotaxi service in order to compare their relative competitiveness under different operating conditions. The models reveal that labor remains a significant cost for robotaxi services but that robotaxi operating costs are still lower than those of traditional taxi services. Ultimately, utilization rates and annual mileage are the most influential factors for robotaxi competitiveness. Purported cost savings of AVs, as well as other capital-intensive AI technologies, must thus be considered in light of their remaining labor costs and intended operational contexts.AV impacts, as well as those of other AI-enabled technologies, will ultimately depend on the extent to which people choose to adopt them. Some researchers fear that robotaxi services could compete with not only taxi and ride-hailing services, but also public transit services if vehicle automation enables significant price decreases. The third study in this dissertation uses responses from an online choice-based conjoint survey fielded in the Washington, D.C. Metropolitan Region (N = 1,694) in October 2021 to estimate discrete choice models of public preferences for different autonomous (ride-hailing, shared ride-hailing, bus) and non-autonomous (ride-hailing, shared ride-hailing, bus, rail) modes. The estimated models are then used to simulate future marketplace competition across a range of trip scenarios. On average, respondents were only willing to pay a premium for autonomous modes when a vehicle attendant was also present, suggesting that the presence of an attendant may be a critical feature for early AV adoption. Additionally, scenario analyses revealed that transit remained competitive with autonomous ride-hailing modes for trips where good transit options were available. These results suggest that fears of a mass transition away from transit to AVs may be limited by people's willingness to use AVs, at least in the short term. Moreover, these findings highlight the importance of investigating multiple design factors that might influence public adoption of emerging AI technologies.Taken together, this dissertation examines AVs not simply as a technology, but as an element of a complex, sociotechnical system. Building on prior work, this research seeks to address gaps regarding how AI-enabled AVs are reshaping tasks and labor roles, changing the economics of existing services, and altering public preferences for different transportation modes. AI technologies and their societal impacts are not predetermined but rather emerge from a process of mutual shaping between technology and society. The aspiration with AVs is that they can help address longstanding problems with our existing transportation systems. By gaining early insights into the impacts of AVs and other AI technologies, we create the opportunity to proactively shape outcomes toward desirable futures.

English

ISBN: 9798384025696Subjects--Topical Terms:

558117
Transportation.
Subjects--Index Terms:

Autonomous vehicles

AI Behind the Wheel: Work, Economics, and Preferences in the Era of Autonomous Vehicles /
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This dissertation unpacks several potential impacts of AVs and uses them as a case study of the potential societal effects of AI-enabled automation in the physical world.AI advancements are changing the ways in which automation technologies can interact with and displace human labor. Unlike routine manual tasks like repetitive assembly that have undergone substantial substitution by conventional automation technologies, non-routine physical tasks have historically had limited opportunities for substitution or complementarity. AI advancements, however, are expanding automation technologies' capabilities within the non-routine task realm, demanding research into their resulting labor impacts. The first study in this dissertation uses direct observations, semi-structured interviews, and archival data to identify and map how tasks and labor roles change in response to the introduction of autonomous vehicles to a taxi work system. This detailed analysis empirically demonstrates that introduction of AI technologies spurs the rebundling of tasks and reorganization of adjacent labor roles, and reveals three archetypal patterns of role change: distributing, consolidating, and scaffolding. These patterns can be used to refine labor outcome analyses and predict labor impacts at the task, role, and sector or economy level.What motivates the substitution of a human driver with an AV system? While the promise of safer road travel is an oft cited motivation, the lure of lower operating costs is arguably the primary driver of investment in AV development. For years, Transportation Network Companies (TNCs) like Uber and Lyft, and emerging AV companies like Waymo, Cruise, and Zoox, have raised billions of dollars by promising a future of autonomous taxi fleets (robotaxis) that would eliminate the cost of a driver and pave a path to profitability. The future operating costs of robotaxi services, however, remain highly uncertain. Prior studies estimating the future costs of robotaxi services lack precision in two key areas: 1) the cost of autonomous vehicle technologies and 2) the labor costs needed to operate a robotaxi service. These two cost categories are critical as the payoff to firms for automating a particular task depends on the cost of the technology relative to the cost of the worker who performs that task.The second study in this dissertation draws on the first study's findings about changing labor roles to develop ground-up cost models for a traditional taxi and a robotaxi service in order to compare their relative competitiveness under different operating conditions. The models reveal that labor remains a significant cost for robotaxi services but that robotaxi operating costs are still lower than those of traditional taxi services. Ultimately, utilization rates and annual mileage are the most influential factors for robotaxi competitiveness. Purported cost savings of AVs, as well as other capital-intensive AI technologies, must thus be considered in light of their remaining labor costs and intended operational contexts.AV impacts, as well as those of other AI-enabled technologies, will ultimately depend on the extent to which people choose to adopt them. Some researchers fear that robotaxi services could compete with not only taxi and ride-hailing services, but also public transit services if vehicle automation enables significant price decreases. The third study in this dissertation uses responses from an online choice-based conjoint survey fielded in the Washington, D.C. Metropolitan Region (N = 1,694) in October 2021 to estimate discrete choice models of public preferences for different autonomous (ride-hailing, shared ride-hailing, bus) and non-autonomous (ride-hailing, shared ride-hailing, bus, rail) modes. The estimated models are then used to simulate future marketplace competition across a range of trip scenarios. On average, respondents were only willing to pay a premium for autonomous modes when a vehicle attendant was also present, suggesting that the presence of an attendant may be a critical feature for early AV adoption. Additionally, scenario analyses revealed that transit remained competitive with autonomous ride-hailing modes for trips where good transit options were available. These results suggest that fears of a mass transition away from transit to AVs may be limited by people's willingness to use AVs, at least in the short term. Moreover, these findings highlight the importance of investigating multiple design factors that might influence public adoption of emerging AI technologies.Taken together, this dissertation examines AVs not simply as a technology, but as an element of a complex, sociotechnical system. Building on prior work, this research seeks to address gaps regarding how AI-enabled AVs are reshaping tasks and labor roles, changing the economics of existing services, and altering public preferences for different transportation modes. AI technologies and their societal impacts are not predetermined but rather emerge from a process of mutual shaping between technology and society. The aspiration with AVs is that they can help address longstanding problems with our existing transportation systems. By gaining early insights into the impacts of AVs and other AI technologies, we create the opportunity to proactively shape outcomes toward desirable futures.
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