Geospatial Information and Knowledge Discovery (GIKD) of the co-PIs and their students: With large, diverse geospatial datasets and an integrated, user-centric interface to analyze user-defined algorithms, the Instrument will give researchers the opportunity to experiment with spatially-aware information and knowledge discovery algorithms. This will allow geospatial pattern mining and discovery of interesting geospatial patterns on spatial objects in rare event detection, spatio-temporal change and trend detection, and correlation mining. For example, in rare event detection, unpredictable events are extremely difficult to detect because they don't occur often or they occur at a time/location where they are not expected (e.g., detecting traffic congesting during a disaster situation).

The Instrument will provide researchers with historical data that is used to establish a baseline for dynamic event behavior models, and scenarios when deviations from the normal model are identified. Algorithms can then be built to better enable prediction models. These can be input into the Instrument for testing and future rare event detection and prediction. The Instrument will also aid in the investigation of bootstrapping techniques [CBO+02, JDC87] and cost-sensitive learning approaches [Elk01,ZE01] for rare event detection in spatial data with semantic awareness.

The Disaster and Failure Studies Program at NIST (the National Institute of Standards and Technology) [http://www.nist.gov/el/disasterstudies/index.cfm] (see letter) will utilize the Instrument for their Disaster and Failure Events Data Repository. The Instrument's mapping and query systems will also enable research in earthquake analytics through enhanced spatio-temporal querying of earthquake data mashed up with multi-temporal imagery and many archival and real-time datasets, ranging from demographics to historical traffic speeds in street segments.

The Mobile Information Systems Research Center at the University of Illinois at Chicago [http://www.cs.uic.edu/~wolfson/] (see letter) will utilize the Instrument for their research on moving objects.

The Instrument will support the FIU effort under the just announced $11.4M USDOT TIGER award (http://cake.fiu.edu/TIGER2013) led by these co-PIs to develop a sustainable community of UniversityCity and to alleviate traffic congestion.  The proposed data collection instrument will further facilitate the expansion of the TIGER benefits from the UniversityCity to all of South Florida and make it an adaptable role model for the Nation.

The utilization of the Instrument in application leading to Florida road decongestion is referenced in the letters from the executive leadership of Florida Department of Transportation Secretary, South Florida Regional Planning Council, Miami-Dade County, Miami-Dade Expressway Authority, and City of South Miami. The Instrument will also enable advanced research by our faculty in conjunction with the DOT Project.

The analysis of the data managed by the system could result in predictive traffic estimates that would strengthen the Informed Traveler Program and Applications (ITPA). By analyzing real-time streams of video of critical traffic intersections, the instrument will enable ITPA users to be better informed about transportation modes and routes to best meet their needs.

A particular research direction is multi-modal traffic modeling: Car, bus, bicycle and pedestrian traffic congest subject area during peak hours and cause significant loss of time and energy. The congestion is due to both the intensity of traffic and the interference between the different flows of traffic (pedestrian, cars, etc.).

We will investigate the alleviation of this problem in two steps using the MRI instrument. The first step is to develop a simulation of traffic using the MRI Instrument's advanced aerial-image processing. Advanced aerial imaging is a non-invasive and inexpensive traffic-sensory method which can be much more efficient than ground-level cameras or car detectors. The MRI Instrument will store and perform feature identification on O(10 minute)-frequency high-resolution balloon aerial images.

We will utilize the feature-recognition of the Instrument to identify cars, pedestrians, bicycles and buses. These features will be abstracted from the images by the Instrument and be used to develop a transit simulation model for the area. The traffic model/simulation will then be used in the ITPA. The system would consist of management of traffic-lights and recommendation of parking lots to commuters (through their smart-phones) based on real-time analysis of traffic using the Instrument. The ITPA would act to decongest all forms of traffic simultaneously by optimizing traffic-light periods within the area and by making smart recommendations for parking lots along least-congested routes.

References Cited

[CBO+02] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. "SMOTE: Synthetic Minority Over-sampling TEchnique." Journal of Artificial Intelligence Research, 16:321-357, 2002.

[JDC87] A. K. Jain, R. C. Dubes, and C.-C. Chen. "Bootstrap techniques for error estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence, 9:628-633, 1987.

[Elk01] C. Elkan. "The foundations of cost-sensitive learning." In IJCAI, pages 973-978, 2001.

[ZE01] B. Zadrozny and C. Elkan. "Learning and making decisions when costs and probabilities are both unknown. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 204-213, 2001.