Even with advances in GPS navigation, real-time traffic alerts and mapping, daily commute times are often unreliable, and relevant updates on how to avoid congestion often reach commuters when they are already stuck in traffic and it is too late to change course.
In recent years, IBM researchers have begun to think of traffic as a global data problem.
According to the IBM Global Pain Commuter study conducted last year, the daily commute in some of the world's most economically important international cities is longer and more grueling than before imagined.
But what if you were able to get an email or text message with personalized information on what the traffic patterns of your typical commute look like before your trip even began?
This is a question IBM is answering as part of a new collaboration with the California Department of Transportation and UC Berkeley. Together, they are developing a solution that uses predictive analytics to help commuters avoid traffic congestion and enable transportation agencies to better understand, predict and manage traffic flow. While initially being tested in the United States, IBM is actively working in cities around the world in the area of Smarter Transportation, using a worldwide team of scientists, industry experts and IT services professionals to research, test and deploy new traffic information management capabilities.
Learn more about this project and hear from the lead researcher on this project here.