Forschungsgruppen

Der Lehrstuhl für Vernetzte Verkehrssysteme teilt sich in vier Forschungsfelder ein, je mit ihrem eigenen Fokus, Werkzeugen, und Personal.

This research group focuses on human factors, their impacts on transport, and their interactions within different aspects of the transportation industry.

Key areas of investigation:

  • Driving behavior (driving simulation, naturalistic driving) 
  • Travel behavior (gender impact, socio-demographics) 
  • Survey design 
  • Acceptance of disruptive transport technologies (e.g., UAM, Hyperloop) 
  • User experience evaluation, including comfort assessment in different transportation modes 
  • Behavior modeling for transportation planning and policy 

Current projects:

Past projects:

Members:

Open thesis topics:

  • Mode choice modeling to and from Munich Airport: case study on Urban Air Mobility. Mentoring: Filippos Adamidis and Hao Wu. Download description.​​​​​​​
  • Funded theses opportunities within the Verkehr-SuTra project. Download description.
  • On-road driver behavior data collection and analysis: safety tolerance zone evaluation. Mentoring. C. Al Haddad. Download description.
  • Gender and mobility. Mentoring: C. Al Haddad and M. Abouelela. Download description.
  • Incorporating public transport fare zones in mode choice analysis: Munich case study. Mentoring: Mohammad Sadrani
  • Switching demand between automated car sharing and automated public transport. Mentoring: Mohammad Sadrani
  • An investigation of driver-pedestrian communications for development of external human-machine interfaces (e-HMI). Mentoring: R. Ezzati Amini
  • Investigating the vehicle-pedestrian interactions at unsignalized intersections to support the development of a microscopic agent-based tool for simulating pedestrian behaviours. Mentoring: R. Ezzati Amini

Key areas of investigation:

  • DTA model calibration
  • Redistributing metro demand to alleviate the effects of over capacity
  • Optimizing and modeling dynamic van-pooling services
  • Optimisation-based transportation operations
  • Optimisation-based multimodal freight operations

Members:

Open thesis topics:

  • Assessing charging strategies for electric vehicles: Application of multiple-criteria decision-making methods. Mentoring: Mohammad Sadrani
  • Assessing public transport fare structures and potential alternatives: Novel decision-making techniques. Mentoring: Mohammad Sadrani
  • Control strategies in automated bus fleet operations. Mentoring: Mohammad Sadrani
  • Optimization of internal layout and space: Automated public transport vehicles. Mentoring: Mohammad Sadrani

This research group focuses on modelling and simulating inter/multimodal transportation systems, emerging mobility and vehicle technologies.

Key areas of investigation:

  • Transport demand and supply modeling (traditional and agent-based modeling)
  • Modeling multimodal transportation systems
  • Modeling emerging/on-demand mobility systems
  • Modeling autonomous/connected autonomous vehicles

Current projects:

Past projects:

Tools and frameworks:

Members:

Open thesis topics:

The focus is on the use of publicly available datasets for transport analytics and modeling. Due to the availability of diverse datasets, this group has a wide coverage of topics such as travel demand, traffic behavior, transport supply, traffic safety.

Key areas of investigation:

  • Demand calibration using opportunistic/big data
  • Extracting trip attributes from opportunistic sources
  • Extracting mobility information from Social Media data
  • Data fusion for transportation modelling using opportunistic data
  • Traffic behavior modeling and safety analysis using naturalistic driving data
  • Transport supply modeling using OSM and GTFS data

Current projects:

Past projects:

Members:

Open thesis topics:

  • (Porsche) Bachelor’s Thesis in Automatic Machine Learning for Time-Series Forecasting and Anomaly Detection. Mentoring: Nadin-Katrin Apel. Download description. (release date: 07.09.2022)
  • Data-driven prediction of demand for multimodal trips. Mentoring: Santhanakrishnan Narayanan
  • Leveraging open data and graph neural networks for optimal traffic analysis zoning Vishal Mahajan
  • Transferability of the deep learning based traffic prediction models Vishal Mahajan

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