Get into a cab and it’s safe to assume the driver knows the ins, outs, shortcuts and potential traffic tie-ups between you and your destination. That kind of knowledge comes from years of experience, and IBM is taking a similar tact that blends real-time data and historical information into a new breed of traffic prediction.
IBM is testing the new traffic-management technology in a pilot program in Lyon, France, that’s designed to provide the city’s transportation engineers with “real-time decision support” so they can proactively reduce congestion. Called Decision Support System Optimizer (DSSO), the technology uses IBM’s Data Expansion Algorithm to combine old and new data to predict future traffic flow. Over time the system “learns” from successful outcomes to fine-tune future recommendations.
The company’s technology allows traffic engineers to quickly take action based on constantly updated information, such as putting detours in place or providing alternative routes to get traffic moving after a snag. They’re unable to do this now, according to IBM, since most metro traffic management centers rely only on video feeds and color maps showing real-time traffic conditions. Jurij R. Paraszczak, director of Smarter Cities IBM Research, says this means traffic engineers don’t have a “360-degree view” of traffic, and depending on predefined responses or making reactive decisions, they don’t always fully take into account all current and future patterns.