IBM, Caltrans and UC Berkeley Aim to Help Commuters Avoid Congested Roadways Before their Trip Begins

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 congestion 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 is predicted to be available at the station.

“In order for intelligent transportation systems to be truly effective, travelers need information they can act upon before getting stuck in traffic,” 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 create useful information that focuses on providing timely, actionable information to the traveler.”