Developed by researchers in the university’s Maryland Transportation Institute in conjunction with Commuter Connections, a program of the National Capital Region Transportation Planning Board, the incenTrip app recommends the best travel mode, departure time and route based on both real-time traffic prediction data and user personal preferences to help commuters avoid both day-to-day congestion and traffic jams caused by accidents, work zones, special events and adverse weather.
The app, incenTrip, provides monetary and non-monetary incentives to improve commuter trips and reduce traffic congestion, energy use, and emissions. It not only recommends alternative departure times and travel routes, but provides personalized travel rewards points when users opt for greener mode choices such as carpooling, mass transit, walking, or biking. Support for the travel incentive program is being provided by MWCOG, TPB, and Commuter Connections together with their state transportation department partners.
Combining artificial intelligence of individual user preference with real-time prediction of transportation system dynamics, incenTrip learns its users’ travel patterns, allowing it to make individually-tailored recommendations. Together, incenTrip’s features add up to a powerful, cost-effective way to influence travel behavior, says Herbert Rabin Distinguished Professor Lei Zhang, MTI’s director.
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