Deutsche Autoindustrie und autonomes Fahren

Bardt, Hubertus

doi:10.1007/s10273-016-2048-3

Cited by 10 publications

(7 citation statements)

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“…A study conducted by Zhang and Guhathakurta [14] estimates that with only 5% of the trips served with SAVs, the parking land use in Atlanta could be reduced by 4.5%. A similar reduction was found with the case of Lisbon, where a 100% shared self-driving taxibot fleet could reduce parking spaces by more than 90% [8].…”

Section: The Impact Of Avssupporting

confidence: 80%

“…In the later study of Fagnant and Kockelman [7] which was based on a simulation of Austin, Texas, they estimated that one SAV could replace 10.77-11.53 cars, depending on how many trips are shared between passengers. Similarly, an OECD [8] study found that the combination of TaxiBots together with high-capacity public transportation would remove 9 out of 10 cars in mid-sized cities in the case of the city of Lisbon. These studies have also analyzed the impact on emissions and total travel kilometers.…”

Section: The Impact Of Avsmentioning

confidence: 97%

“…In the EU, approximately one-fifth of greenhouse gas emissions is produced by road transport (https://www.eea.europa.eu/data-and-maps/indicators/transport-emissions-of-greenhousegases/transport-emissions-of-greenhouse-gases-12). Reduction of emissions due to automation has been estimated by several studies [6,9] but it will also depend on what kind of technology will be used [8]. Studies have also estimated different impacts on the total amount of travel kilometers.…”

Section: The Impact Of Avsmentioning

confidence: 99%

“…The car industry will especially be under heavy pressure if SAVs will dominate over private AVs, as the sale of cars to private individuals, which is the current main business for car manufacturers, will drop significantly. On the other hand, the increased mileage and active service hours per SAV will shorten the life cycle of the vehicles, which, in turn, creates additional demand [8,9]. The deployment of AVs and SAVs also require large investments in infrastructure which has an impact on the economy.…”

Section: The Impact Of Avsmentioning

confidence: 99%

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Mobility Acceptance Factors of an Automated Shuttle Bus Last-Mile Service

Soe

Müür

2020

Sustainability

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The main interest of this paper is to analyze the mobility acceptance factors of an automated shuttle bus last-mile service. There is limited research on the passengers’ perception of security and safety of automated mobility, whereas prior research is mostly based on surveys interested in attitudes towards self-driving vehicles, without being linked to the experience. We, on the other hand, are interested in passengers’ feeling of security and safety, after taking a ride with an automated shuttle in an open urban environment. For studying this, we conducted an automated shuttle bus last-mile pilot during a four-month period in the city of Tallinn in late 2019. The method is a case study focusing on one city with several tools for data collection applied (surveys, interviews, document analysis). The pilot, open and free for everybody, attracted approximately 4000 passengers, out of which 4% responded to the online feedback survey. For studying the operational capacity, we had a panel interview with operators of the shuttle service, in addition to analyzing daily operational log files. The results indicate that passengers’ perceived feeling of security and safety onboard was remarkably high, after taking a ride (and lower without a ride, in a different control group). The bus was operated only if operational capacity was secured, thus having significant downtime in service due to environment, technology and traffic-related factors.

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Section: The Impact Of Avssupporting

confidence: 80%

Section: The Impact Of Avsmentioning

confidence: 97%

Section: The Impact Of Avsmentioning

confidence: 99%

Section: The Impact Of Avsmentioning

confidence: 99%

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Mobility Acceptance Factors of an Automated Shuttle Bus Last-Mile Service

Soe

Müür

2020

Sustainability

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“…However, similarly to Milakis et al [5], they also found that they may increase the total VMT by 10% compared to nonautomated modes. On the contrary, some scholars argue that AVs have the potential to reduce road accidents, have positive environmental impacts, reduce cost of travel, as well as land use benefits and the potential to increase comfort and mobility of immobile members of the society [13,24,25]. According to the OECD, the parking requirements of SAVs in Lisbon are expected to decrease by 84-94% compared to today's land use composition [25].…”

Section: Possible Impacts Of Av Assimilationmentioning

confidence: 99%

Modeling Cross-National Differences in Automated Vehicle Acceptance

et al. 2020

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The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed.

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Smart Cities and Smart Regions Roadmap

Soe

2023

Advances in Sustainability Science and Technology

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Deutsche Autoindustrie und autonomes Fahren

Cited by 10 publications

References 0 publications

Mobility Acceptance Factors of an Automated Shuttle Bus Last-Mile Service

Mobility Acceptance Factors of an Automated Shuttle Bus Last-Mile Service

Modeling Cross-National Differences in Automated Vehicle Acceptance

Smart Cities and Smart Regions Roadmap

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