GPS is useful but there is always room for improvement. Top technology firms are working on ways to combine real-time data with personal route preferences and particular destinations to get drivers to where they are going as efficiently as possible. On February 25, 2010, IBM announced a new research initiative to build personalized travel routes for commuters to avoid traffic gridlock. IBM researchers are using advanced analytics to develop adaptive traffic systems that will intuitively learn traveler patterns and behavior to provide more dynamic travel safety and route information to travelers than is available today.
IBM researchers are developing new models that will predict the outcomes of varying transportation routes to provide a personalized recommendation that get commuters where they need to go in the fastest time. This project intends to provide information that goes well beyond traditional traffic reports, after-the fact devices that only indicate where you are already located in a traffic jam, and web-based applications that give estimated travel time in traffic.
Using new mathematical models and IBM's predictive analytics technologies, the researchers will analyze and combine multiple possible scenarios that can affect commuters to deliver the best routes for daily travel, including many factors, such as traffic accidents, commuter's location, current and planned road construction, most traveled days of the week, expected work start times, local events that may impact traffic, alternate options of transportation such as rail or ferries, parking availability and weather.
IBM will provide the personalized commuting information via the web, through mobile voice interaction, combined with advanced mapping applications on mobile devices.
For example, combining predictive analytics with real-time information about current travel congestion from sensors and other data, the system could recommend better ways to get to a destination, such as how to get to a nearby mass transit hub, whether the train is predicted to be on time, and whether parking is predicted to be available at the train station. New systems can learn from regular travel patterns where you are likely to go and then integrate all available data and prediction models to pinpoint the best route.