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@PHDTHESIS{Schrmbges:998597,
author = {Schrömbges, Michael Sebastian},
othercontributors = {Kuhnimhof, Tobias Georg and Eisenmann, Christine},
title = {{A}uswirkung von {F}ahrzeugautomatisierung auf den
{P}kw-{B}estand: eine {P}rognose anhand eines
{P}kw-{V}erfügbarkeitsmodells},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-11474},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2025; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2024},
abstract = {Private passenger cars are the most common means of
transportation (Eurostat 2023; infas, DLR et al. 2019b; ITF
2023), but they hinder mobility transition and climate
protection goals (UBA 2023). Consequently, the objective of
transportation planning must be to regulate the growth of
car ownership in the future. In this context, the emergence
of highly automated vehicles could facilitate two
developments: On the one hand, highly automated private cars
promote motorized individual transport (MIT), as travel time
can be used differently and parking is made easier
(Gkartzonikas, Gkritza 2019; Nordhoff, Kyriakidis et al.
2019). On the other hand, highly automated shared vehicle
fleets can complement public transport by improving
accessibility to destinations (Dianin, Ravazzoli et al.
2021). However, it remains uncertain whether automated
private cars will increase the number of cars or whether
automated vehicle fleets will replace private vehicles.
Against this background, the objective of this study is to
examine the relationship between vehicle automation and car
ownership. To this end, a model of car availability in
Germany is developed, which includes information on the
transportation supply. As vehicle automation will enable
boarding and alighting at individually chosen locations and
improve the accessibility of destinations, the
transportation supply will accordingly change. These changes
are examined in vehicle automation scenarios for the year
2050 using the car availability model. In the developed car
availability model, vehicle automation affects the
transportation supply as follows: Highly automated private
cars enable valet parking and could improve the nationwide
accessibility of destinations by nearly five times compared
to conventional cars through a change in the perception of
travel time. This would increase the attractiveness of
highly automated private cars, potentially leading to a
growth in the car fleet by up to $+8\%.$ Conversely, highly
automated shared vehicle fleets could enhance accessibility
to destinations by nearly fourfold compared to current
public transportation if implemented throughout Germany.
Particularly, feeder services to train stations would have a
greater impact than direct door-to-door connections. The
introduction of highly automated vehicle fleets could reduce
the car fleet by up to $-2.4\%.$ However, it is likely that
both scenarios would occur simultaneously rather than
separately. In this case, the increase in the car fleet due
to highly automated private cars would not be offset by the
decrease due to highly automated shared fleets. Therefore,
the car fleet would likely grow by approximately $+4\%$ in
the future. In order to develop appropriate measures for
managing car ownership in the future, forecasting models
should consider the effects of changes in transportation
supply on car availability. In particular, regulating
parking space availability could be an effective way to
influence the car fleet. Additionally, when improving public
transportation services, priority should be given to
providing good connectivity to train stations. In
conclusion, the car availability model presented in this
study offers a suitable tool for planning and developing
measures that could sustainably influence future car
ownership, thereby supporting the mobility transition and
climate protection goals.},
cin = {313310},
ddc = {624},
cid = {$I:(DE-82)313310_20140620$},
pnm = {BMBF 01UV1901B - Japanisch-deutsche Forschungskooperation
zum vernetzten und automatisierten Fahren: Sozioökonomische
Folgenabschätzung - Teilprojekt B: Auswirkungen von CAD auf
den Pkw-Besitz (01UV1901B)},
pid = {G:(BMBF)01UV1901B},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2024-11474},
url = {https://publications.rwth-aachen.de/record/998597},
}
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