[IMAGE]Calif-traffic-2.jpg[/IMAGE]IBM announced a new collaboration with the California Department of Transportation (Caltrans) and California Center for Innovative Transportation (CCIT), a research institute at the University of California, Berkeley, to help commuters avoid congestion before their trip begins and enable transportation agencies to better understand, predict and manage traffic flow.
In a technology advance that will ultimately help drivers around the world avoid rush hour traffic jams, IBM Research has developed a new predictive modeling tool that will let drivers quickly access personalized travel recommendations to help them avoid congestion and save time and fuel.
By joining forces, IBM, Caltrans and the Mobile Millennium team within the CCIT hope to provide drivers with valuable predictive information on what traffic patterns are likely to look like — even before they leave work or home and get in their vehicles — rather than discover what has already happened and is being reported.
Using this predictive and analytic traffic tool, transportation agencies and city planners in the future will be able to proactively design, manage and optimize transportation systems to deal with ever-increasing traffic due to population growth and increasing urbanization.
Traffic delays caused by highway incidents such as work zones, crashes or simply by morning and evening rush hours routinely stymie frustrated drivers. 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.
This inability to avoid traffic congestion has led to commuters across the U.S. wasting on average almost a week's worth of time, 28 gallons of gas and $808 over the course of a year. (Source: http://mobility.tamu.edu/ums/report/congestion_cost.pdf )
In Silicon Valley, the problem is especially acute. In comparison with cities of a similar size in population, drivers in the city of San Jose waste a cumulative of 10 million more annual hours sitting in traffic jams and suffer a 15 percent higher commute delay per peak-time traveler. (Source: http://mobility.tamu.edu/ums/congestion_data/)
Spanning the San Francisco Bay Area Region, the new Smarter Traveler research initiative collects and analyzes traffic data generated from existing sensors in roads, toll booths, bridges and intersections. This unique project combines that data with locations based on GPS sensors in participant’s cell phones to learn their preferred travel days and routes. Alerts are then automatically delivered via email or text message on the status of the driver’s typical commute before the trip begins, which eliminates potential distraction once a driver is on the road.
