The need for optimization of the transportation system is driven by increasing urbanization, economic developments, technological progress, and the need for environmental sustainability. To effectively deal with these challenges, transportation solutions must move beyond the traditional “point in time” optimization approach, to a more dynamic planning approach in which plans and their realization are continuously adjusted based upon new transportation offers, improvements to the current transportation system, and dynamic network constraints. Emerging mobility concepts such as autonomous vehicles must be integrated into traffic models as they show great potential to dramatically change the transportation network and system performance.
We develop data-driven approaches for modeling, analysis, and decision-support in a mobility system that caters to the needs of people. Our research focus is to predict the movement of people in multi-modal systems to ensure seamless trips and thus manage mobility as an integrated system rather than loose connection of different mobility services. We address the necessity for improved demand management through persuasive strategies that trigger long-term intrinsic behavior change through the incorporation of human factors into the decision models. This approach offers the potential to improve the efficiency of measures while avoiding rebound effects.
Our research investigates the introduction of new mobility concepts, such as Mobility as a Service (MaaS), autonomous vehicles or Urban Air Mobility (UAM), into a transportation system to help in reaching the expected opportunities like raising the capacity of the transport system, reducing traffic congestion and increasing, efficiency, safety and comfort. As a basis we develop novel modeling approaches that can be directly applied by different stakeholders to remove many uncertainties that make investment and policy decisions related to land use and transportation planning challenging. We investigate the interaction between different mobility modes including pedestrians and autonomous vehicles and incorporate them into advanced simulation and planning tools.
AIT’s research activities include:
- Flexible mobility simulation environments including mesoscopic, agent-based transport modelling tools and microscopic transport simulations
- Modeling and simulation of mobility measures and human choices and behavior applying statistical modelling as well as deep learning techniques
- Linking human reactions to environmental influences, new technologies, changes in the transport infrastructure or transport safety issues to the overall transport system
- Model calibration and validation through comprehensive mobility behavior datasets from experiments and real-world measurements as well as mobility surveys
- Innovative crowd simulations to predict complex pedestrian flows for multi-scale analysis
- Modeling of new mobility concepts such as autonomous vehicles, MaaS, rural micro transit, and UAM