As part of the newly established interdisciplinary research group ReSpace! – Connected Response-able Spaces and Infrastructures for Sustainable Living, this doctoral research project focuses on the simulation-based exploration of future urban mobility systems under conditions of climate change and spatial scarcity. The aim is to model how passenger and freight transport can be dynamically coordinated through multifunctional hubs, flexible vehicle fleets, and digitally connected infrastructures. Using agent-based methods (MATSim), the project investigates how spatio-temporal decoupling and multimodal integration strategies affect the efficiency, accessibility, and resilience of transport systems. Particular attention is given to evaluating real-world applicability through scenario-based planning, infrastructure modelling, and performance-based analysis of transformation pathways.
This research position focuses on the agent-based simulation of flexible mobility services such as demand-responsive transport (DRT) in rural and peri-urban regions. The work explores how DRT systems, potentially operated with automated vehicles, can be spatially and temporally configured to complement existing public transport. Using MATSim, the simulation framework models individual travel behavior and system interactions to evaluate integration strategies, infrastructure requirements, and impacts on accessibility, efficiency, and environmental performance. The goal is to derive evidence-based recommendations for planning inclusive and sustainable rural mobility systems under realistic transition scenarios.
SEED is a collaborative research project funded by the German Research Foundation (DFG), jointly conducted by the Institute of Transportation and Urban Engineering at Technische Universität Braunschweig and the Institute for Road and Transport Science at Universität Stuttgart. The project investigates the potential of floating car data (FCD) as a single-source basis for the estimation and calibration of travel demand models.
Unlike conventional approaches that rely on costly and infrequent travel surveys, SEED develops and evaluates data-driven methods to infer origin-destination matrices directly from probe vehicle data. The aim is to enable endogenous demand estimation from observed traffic patterns and to enhance the calibration and validation of macroscopic and agent-based transport models. The project is expected to start in October 2025; the exact employment start date is negotiable.