One of the main barriers to a better understanding of activities and travel patterns is the difficulty in collecting long-duration data. Previous studies have examined computer-aided interview techniques. Others have researched the potential for global positioning system (GPS) antennas to collect more accurate travel data. This dissertation combines these two techniques by adding the use of wireless communications technology to integrate streaming, real-time GPS data with a dynamically generated, web-based activity survey. In addition, three separate analysis techniques are examined using the results of an informal pilot test. The purpose of these analysis techniques is to weave together the large set of GPS data that can be collected with the much smaller set of activity responses that can be expected. The net result represents both an advance in data collection techniques, as well as a new, peer-to-peer approach to gathering and sharing experiential transportation information, an approach that should be incorporated into future Intelligent Transportation Systems designs.
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