AIT Austrian Institute of Technology, Vienna
March 11, 2016, 12:15, Room GC B3 30 (click here for the map)
Microscopic simulation models are used in many applications for predicting pedestrian flows with high granularity. Current simulators do not allow for easy and quick switching between models. Moreover, reliable human movement data is still sparse, which is a prerequisite for model calibration and validation. These shortcomings inhibit to evaluate the capabilities of different models. This talk presents a unified framework for the structured investigation on strengths and weaknesses of microscopic pedestrian simulation models. The empirical baseline is a highly accurate benchmark data set measured under real life conditions in a bidirectional corridor with a novel data collection approach using the Microsoft Kinect. The proposed simulation framework is built on a scalable and flexible system architecture to easily integrate different models. The results highlight individual capabilities of seven different modeling approaches to represent microscopic and macroscopic characteristics of human movement behavior.
Dr. Stefan Seer is leading the �Dynamic Crowd Solutions� research group at the AIT Austrian Institute of Technology in Vienna. He was a visiting researcher with the SENSEable City Lab at the Massachusetts Institute of Technology. He is the coordinator of the research collaboration between the AIT and the MIT, where he currently leads a project on �Persuasive Urban Mobility� together with the MIT Media Lab and a project on �Perception Based Modeling� together with the MIT SENSEable City Lab. He was awarded the �National Award for Traffic 2008� by the Austrian Ministry for Transport, Innovation and Technology in the category �Logistic Traffic Solutions at Major Events� for an innovative computer-aided crowd control system to optimize pedestrian flows in public transport infrastructures. He has specialized for more than 10 years in the development of models and algorithms for pedestrian flow simulation also including technologies to collect and analyze data on crowd behavior in the context of urban transportation. He has a Master's Degree in Electronics Engineering and holds a Doctoral Degree in Computer Science from Vienna University of Technology.