Unsupervised Summarization and Change Detection in High-Resolution Signalized Intersection Datasets

The modern road network infrastructure (signal controllers and detectors) continuously generates data that can be transformed and used to evaluate the performance of signalized intersections. In order to automatically make meaningful observations about signal performance, we propose the application of data summarization and compression techniques in order to intelligently group together intersections and/or time intervals during the day and certain days of the week. This work details the use of linear and nonlinear dimensionality reduction techniques to achieve the aforementioned goals. The approach is also extended to perform change detection so that significant changes at intersections and corridors can be highlighted.