IBM
today 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.
“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,” says Greg
Larson, chief of the Office of Traffic Operations Research, Caltrans.
“Together with partners like CCIT and IBM we’re driving a new age of
science-based, data-centric traffic management that will give commuters
the benefit of knowing the fastest, most cost-effective and eco-friendly
route to their destination.”
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
United States wasting on average almost a week’s worth of time, 28 gallons of gas and $808 over the course of a year
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% higher commute delay per
peak-time traveler.
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.
These
alerts will enable drivers to plan and share alternative travel routes,
improve traveler safety and help transportation authorities better
predict and reduce bumper-to-bumper traffic before it occurs through
improved traffic signal timing, ramp metering and route planning.
The
researchers will leverage a first-of-its-kind learning and predictive
analytics tool called the IBM Traffic Prediction Tool (TPT), developed
by IBM Research, which continuously analyzes congestion data, commuter
locations and expected travel start times throughout a metropolitan
region that can affect commuters on highways, rail-lines and urban
roads. Through this Smarter Traveler research initiative, scientists
could eventually recommend better ways to get to a destination,
including directions to a nearby mass transit station, whether a train
is predicted to be on time and whether parking may be available at the
station.
“Unlike
existing traffic alert solutions, we’re helping take the guesswork out
of commuting,” said Stefan Nusser, Functional Manager, Almaden Services
Research, IBM. “By actively capturing and analyzing the massive amount
of data already being collected, we’re blending the automated learning
of travel routes with state-of-the-art traffic prediction of those
routes to give travelers timely information that can help them make
decisions about the best way to get to their destination.”
