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REU 2007-Automated Mode Detection Jeremy S. Weinstein, Mentor: Sean Barbeau Sample Trip Images Car Introduction In the past, census data was collected on travelers using tedious surveys. Users would often neglect to fill out these surveys or remember information incorrectly after their trip. Eliminating the need for this survey will lead to an increase in productivity for anyone using this information on transit. One important question on every survey was “What was your mode of transportation.” Point in Buffer Road Map (Tampa) Bus Walk The Point In Buffer Solution takes a thick line which represents the location of a bus route and compares it to all the points of a trip. This determines if the user is on a bus. This method is likely to be more effective when combined with a Critical Point algorithm, but less effective as a large scale project. A Neural Network is a program which is trained by being given input on various trips until it learns the difference between modes due to subtle differences such as change in speed, distance between stops, dwell time, etc. The Point In Buffer may give the greatest accuracy during the testing phase, but it is problematic in the real world. Any bus company that does not share route information would be seen as a car. Also, initial testing of this program indicated that it may require too much processing time to be viable if made public to multiple users. The Neural Network, on the other hand, has become increasingly promising as more test data is obtained. Thus far it has a 87% accuracy with bus, car, and walking as the three modes to identify (July 18, 2007). This will likely increase as more data is obtained for training. However, if a Critical Point algorithm is applied to the phone, this may go down drastically. Methodology Two possible methods to automatically determine the mode of transportation are the Point In Buffer Solution and the Neural Network Solution. Each method was used in programs which attempted to detect the mode of a traveler. The differences between bike, walk, and automobile can generally be determined with speed. The complexity lies in the difference between a car or a bus. Results Neural Network (Concept) Acknowledgements Special thanks to the TRACIT team at CUTR from USF for their work, expertise, and assistance, the USF REU program for the opportunity, and Dr. Rafael Perez for his support. References I. Witten and E. Frank "Data Mining: Practical machine learning tools and techniques", 2nd Edition, Morgan Kaufmann, San Francisco, 2005. http://www.cs.waikato.ac.nz/~ml/weka/ July 2007 http://edn.esri.com/ June 2007. Department of Computer Science & Engineering