Research Groups

The Chair of Transportation Systems Engineering is divided into four research fields, each with its own focus, tools, and personnel.

Human Factors

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:

  • On-road driver behavior data collection and analysis: safety tolerance zone evaluation. Mentoring. K. YangC. Al Haddad, or R. Ezzati Amini. Download description.
  • Driver behaviour on highways in congested states: A comparison of German and American drivers. Mentoring: A. Ryan and V. MahajanDownload description.
  • A data- and demand-based approach for hyperloop network identification at German and European levels. Mentoring M. Abouelela and C. Al Haddad
  • 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
  • Impacts of weather and built environment on active transportation travel behavior. Mentoring: Jia Guo
  • User preferences for Shared Mobility. Mentoring: G. Cantelmo C. Al Haddad
  • 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

Transport Optimization

Key areas of investigation:

  • Redistributing metro demand to alleviate the effects of over capacity
  • Modelling of dynamic van-pooling
  • Optimisation-based transportation operations
  • Optimisation-based multimodal freight operations

Open thesis topics:

  • Investigation of the effect of autonomous vehicles in mixed traffic conditions. Mentoring: P. Papantoniou
  • Micromobility demand forecasting: Combining big-mobility, opportunistic and contextual data to estimate the demand for micromobility services. Mentoring G. Cantelmo
  • Activity-Based & analytical models for Demand Estimation. Mentoring: G. Cantelmo and M. Qurashi
  • Using Machine Learning to approximate traffic assignment. Mentoring: G. Cantelmo
  • Optimization algorithms for emerging Active Transportation Management measures.  Mentoring: K. Yang

Modeling and Simulation

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:

  • Initial transport model creation for SimMobility. Mentoring: R. Rothfeld
  • Demand data disaggregation and aggregation procedures for strategic models, to interact with fleet management algorithms. Mentoring: Santhanakrishnan Narayanan
  • Modelling of car-ownership. Mentoring: Santhanakrishnan Narayanan
  • Development of calibration procedures for data-driven models. Mentoring: Santhanakrishnan Narayanan
  • Modelling the impacts of reservation based SAV services. Mentoring: Santhanakrishnan Narayanan
  • Modeling autonomous mobility on demand (AMoD) in SUMO by integrated scheduling algorithms. Mentoring: Moeid Qurashi
  • Is AMoD the future: modeling autonomous mobility on demand (AMoD) versus public transport comparison for last mile transport in SUMO. Mentoring: Moeid Qurashi
  • Exploring on-street parking modelling within a microscopic traffic simulation. Mentoring: S. Gomari
  • Estimating on-street parking pressure using data from vehicles - a machine learning approach. Mentoring: S. Gomari
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Transport Data Analytics

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:

Open thesis topics:

  • Driver behavior modeling using Naturalistic dataset. Mentoring: Vishal Mahajan
  • Mobility and activity pattern analysis during the COVID-19. Mentoring: Vishal Mahajan
  • Crash risk analysis in connected and autonomous vehicle environments. Mentoring: K. Yang
  • The evolution mechanism of traffic congestions and its impact on normal traffic.  Mentoring: K. Yang
  • The exploration of the accident mechanism.  Mentoring: K. Yang
  • Using big data mining based on incremental learning for crash risk prediction models.  Mentoring: K. Yang
  • The impact of traffic interventions on drivers' behaviour.  Mentoring: K. Yang

See our research showcase for illustrative examples of the research that is being conducted at the Chair of Transportation Systems Engineering.