Analysis of pedestrian group behavior based on tracking data and pattern recognition methods
Responsable(s) :
Marija Nikolic, Evanthia Kazagli, Michel Bierlaire
Description :
The objective of this project is to analyze the group behavior among pedestrians based on individual trajectory data. The data is collected in Lausanne train station, where a large-scale network of smart sensors has been used to track pedestrians. The project aims to improve the understanding of group behavior among pedestrians and its impact on pedestrian dynamics. It involves the following steps: (i) Development/selection of suitable methods (e.g. data mining/pattern recognition) for identification of individuals walking together, based on pedestrian trajectory data, and their implementation. (ii) Analysis focused on the behavior of identified groups; (iii) Comparison of the findings with the existing empirical basis, as well as with the proposed theories and models that take group dynamics into account. The student needs to have good programming skills (Scala/Matlab), and knowledge of statistical analysis.
Collaboration with:
Type :
semester project
Pré-requis :
Scala/Matlab (good knowledge), statistical analysis, data mining/pattern recognition
Submitted on :
September 21, 2017