Caltrans, UC Berkeley and IBM hope to improve the reliability of estimated commute times, and give drivers personalized travel recommendations that save time and fuel.
With private-sector backing, California’s state government and the state’s flagship public university are teaming up to develop an intelligent transportation solution that will help drivers avoid congested traffic.
The California Department of Transportation (Caltrans), the University of California, Berkeley’s California Center for Innovative Transportation, and IBM Research hope to improve the reliability of estimated commute times, and give drivers personalized travel recommendations that save time and fuel.
U.S. commuters waste 28 gallons of gas and $808 each year because they are stuck in traffic, according to the IBM announcement of the intelligent transportation project. Traffic snarls are notoriously acute in California.
The average person in Los Angeles wasted 38 hours per year in highway traffic jams, according to 2007 U.S. Bureau of Transportation Statistics. In San Francisco, it was 30 hours; in San Diego, it was 29 hours.
“As the number of cars and drivers in the Bay Area continue to grow, so too has road traffic. However, it’s unrealistic to think we can solve this congestion problem simply by adding more lanes to roadways, so we need to proactively address these problems before they pile up,” said Greg Larson, chief of the Office of Traffic Operations Research for Caltrans.
The collaborative research team hopes to give California reliable real-time traffic information before drivers get behind the wheel. “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,” according to IBM.
The company said its researchers have developed a new traffic modeling tool for travelers that continuously analyzes existing congestion data, commuter locations and expected travel start and arrival times throughout a metropolitan region for a variety of transportation modes, including mass transit. The tool could someday recommend the most efficient travel route and also integrate parking information.