IBM Research Initiative Developing Adaptive Systems to Provide Personalized Travel Routes to Avoid Gridlock
IBM has launched 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.
According to the Texas Transportation Institute, as cited by IBM, US traffic congestion burns enough fuel every year to fill 58 supertankers and wastes enough time to consume 105 million weeks of vacation.
New models 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.
Working with state and local transportation agencies, IBM plans to launch pilot projects for select sets of commuters to analyze, test and refine the new systems. IBM plans to provide program participants with 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.
Insight from IBM’s analytics and pilot programs will help transportation agencies better understand and manage traffic, increasing safety on our roads and encouraging the use of efficient public transportation which will help reduce a commuter’s overall carbon output.
The data exists to give commuters and transportation agencies a better way to manage traffic but today it’s not connected. IBM has the ability correlate all of this information to better predict demand, optimize capacity help improve traveler and highway safety as well as reduce our impact on the environment.
—Gerry Mooney, General Manager, Public Sector, IBM
Additionally, IBM is launching a new global virtual Travel and Transportation Center of Competency which will provide new solutions and deep industry expertise for air, rail, truck, and sea transportation.