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Testing the Impact of Personalized Feedback on Household Travel Behavior (TRAC-IT Phase 2)

When presented with their householdís travel patterns and practical ways to improve their trip planning or even eliminate trips, more travelers could find new opportunities to use transit, bicycling or walking, and do less solo driving. This three-phase study harnessed the exponential growth of personal mobile electronic devices integrated with increasingly accurate global positioning systems to develop a non-proprietary location aware information system (TRAC-IT). The conceptual phase used a personal digital assistant equipped with GPS and developed a rule-based expert system to collect travel data and generate travel suggestions. The second phase created a Personal Travel Coach consisting of an enhanced rule-based expert system and a real-time path prediction prototype. The third phase resulted in the first non-proprietary, intelligent software system on GPS-enabled mobile phones to successfully track person movements across all modes and also developed innovative analysis techniques such as purpose and mode detection algorithms. Path prediction allows TRAC-IT to detect potential incidents within the userís probable travel route and provide real-time travel advice to the traveler before they reach the problem areas. The TRAC-IT system should advance the quality and quantity of household multimodal data collected in travel surveys. The TRAC-IT applicationís Personal Travel Coach components; the Expert System and Path Prediction Prototype, give transportation professionals the next-generation location aware information system they need to understand, plan, and influence travel behavior. Technical and policy- based challenges, inherent with such innovative research, are also discussed.

Download final report here

For more information, contact Sean Barbeau at barbeau@cutr.usf.edu or Nevine Georggi at georggi@cutr.usf.edu

06.06.08


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