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@PHDTHESIS{Berghaus:1024561,
author = {Berghaus, Jan Moritz},
othercontributors = {Oeser, Markus and Herty, Michael and García Hernandez,
Alvaro},
title = {{M}icroscopic 2{D}-modeling of driver behavior based on
trajectory data from real traffic, driving simulation and
traffic simulation},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2026-00151},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2026; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {Microscopic traffic flow models are an essential tool for
understanding how driver behavior influences the safety and
efficiency of road traffic, and how road infrastructure can
be designed to support safe and efficient driving behavior.
Microscopic traffic flow models describe a vehicle's
trajectory in two dimensions - longitudinal and lateral -
depending on surrounding traffic and road layout. To reduce
modeling complexity, different model types focus on specific
influencing factors. Car-following models primarily consider
the effect of the leading vehicle on driver behavior,
without accounting for road layout or vehicles in adjacent
lanes. Speed prediction models describe speed choice based
on road geometry, without incorporating surrounding traffic.
Lane change models capture driver behavior before and during
lane changes, taking into account vehicles in the current
and adjacent lanes, but not the road layout. To calibrate
and validate microscopic traffic flow models, various
sources of trajectory data can be used. Real-world
trajectory data yield the most realistic representation of
actual driving behavior, but they do not allow for the study
of behavior under controlled conditions. Driving simulator
data, on the other hand, make it possible to investigate
such controlled conditions. Additionally, synthetic
trajectory data from traffic simulations can be used to
analyze model properties under idealized or specific
conditions. This dissertation investigates how trajectory
data from real-world driving, driving simulation, and
traffic simulation can contribute to more accurate modeling
of driver behavior in two dimensions. The overarching
research question is divided into five sub-questions, each
addressing specific challenges associated with individual
model types and data sources. For car-following models, a
calibration and validation method was developed that enables
the investigation of individual differences between drivers
as well as their behavior in extreme traffic situations
using a driving simulator. A speed prediction model was
applied to evaluate a speed reduction measure in freeway
off-ramps based on real-world trajectory data. For the
modeling of driver behavior in freeway on-ramps, a lane
change model was developed and validated using both
real-world and simulated trajectory data. Furthermore, data
processing methods and quality assessment procedures were
developed for real-world trajectory data, enabling their use
in real-time applications as well as for model calibration
and validation. The models and methods developed in this
dissertation can be applied in traffic simulations to
predict traffic conditions, evaluate planned infrastructure
or traffic control measures, or assess the impact of
automated driving on traffic flow and safety. They thus make
a valuable contribution towards the development of safe,
efficient, and future-ready road systems.},
cin = {313410},
ddc = {624},
cid = {$I:(DE-82)313410_20140620$},
pnm = {DFG project G:(GEPRIS)461365406 - Neue Ansätze der
Verkehrsmodellierung unter Berücksichtigung komplexer
Geometrien und Daten (461365406) / MeBeSafe - Measures for
behaving safely in traffic (723430) / BMBF 01UV2060B -
Verbundprojekt Bürgerlabor Mobiles Münsterland (BueLaMo)
(01UV2060B) / DFG project 280497386 - Grundlagenermittlung
für simulationsgestützte Unfallrisikoabschätzungen -
Mehrskalige Modellierung unter Berücksichtigung dynamischer
Verkehrsflusszustände (280497386)},
pid = {G:(GEPRIS)461365406 / G:(EU-Grant)723430 /
G:(BMBF)01UV2060B / G:(GEPRIS)280497386},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2026-00151},
url = {https://publications.rwth-aachen.de/record/1024561},
}
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