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 

Key projects:

Members:

Open thesis topics:

  • 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 and K. Yang, . Download description.
  • A data- and demand-based approach for hyperloop network identification at German and European levels. Mentoring M. Abouelela and C. Al Haddad
  • Gender and mobility. Mentoring: C. Al Haddad and M. Abouelela. Download description.
  • Travel satisfaction and well-being. Mentoring: Jia Guo
  • Impacts of built environment on shared transport system adoption. Mentoring: Jia Guo
  • Impacts of subjective factors on shared transport system adoption. Mentoring: Jia Guo
  • 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

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 algorithms for emerging Active Transportation Management measures.  Mentoring: K. Yang
  • 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

Key projects:

Tools and frameworks:

Members:

Open thesis topics:

  • Assessment of the impacts of shared mobility services. Mentoring: Santhanakrishnan Narayanan
  • Building and calibrating urban traffic simulation scenario using Open Data. Mentoring: Vishal Mahajan
  • Calibrating public transport scenario in SUMO using Open data. Mentoring: Vishal Mahajan
  • Combining MATSim with Discrete Choice Modells - A Munich Case Study. Mentoring: Raoul Rothfeld
  • Data-driven scheme for optimal Traffic Analysis Zones for transport modeling. Mentoring: Vishal Mahajan
  • Implementing and calibrating the weak lane discipline simulation in SUMO using Open Data. Mentoring: Vishal Mahajan
  • (Taken) Incorporating Micro-Mobility in MATSim and its Networks - A Munich Case Study. Mentoring: Raoul Rothfeld
  • Initial transport model creation for SimMobility. Mentoring: Raoul Rothfeld
  • Is AMoD the future: modeling autonomous mobility on demand (AMoD) versus public transport comparison for last mile transport in SUMO. Mentoring: Moeid Qurashi
  • Modeling autonomous mobility on demand (AMoD) in SUMO by integrated scheduling algorithms. Mentoring: Moeid Qurashi
  • Modelling reservation based SAV services. Mentoring: Santhanakrishnan Narayanan
  • Predictive modeling of driving behavior and vehicle trajectories. Mentoring: Vishal Mahajan

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

Key projects:

Members:

Open thesis topics:

  • Crash risk analysis in connected and autonomous vehicle environments. Mentoring: Kui Yang
  • Crash risk analysis of driver behavior: a simulation study. Mentoring: Kui Yang
  • Data-driven prediction of demand for multimodal trips. Mentoring: Santhanakrishnan Narayanan, Raoul Rothfeld
  • Driver behavior modeling using Naturalistic dataset. Mentoring: Vishal Mahajan
  • Exploratory analysis and modeling of the vehicle location from real-time GTFS. Mentoring:Vishal Mahajan, Raoul Rothfeld
  • Making sense of the mobility-related phenomena at the network scale using machine learning and Open Data. Mentoring: Vishal Mahajan, Raoul Rothfeld
  • Impact of driver characteristics on driver behavior during car-following events. Mentoring: Kui Yang
  • Making use of Open Formats (GTFS) for Public Transport Schedule Data in Germany. Mentoring: Raoul Rothfeld
  • Mobility and activity pattern analysis during the COVID-19. Mentoring: Vishal Mahajan
  • The evolution mechanism of traffic congestions and its impact on normal traffic. Mentoring: Kui Yang
  • The exploration of the accident mechanism. Mentoring: Kui Yang
  • The impact of traffic interventions on drivers' behaviour. Mentoring: Kui Yang
  • Using big data mining based on incremental learning for crash risk prediction models. Mentoring: Kui Yang

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