At the Smart City Expo and World Congress in Barcelona, Spain last week, IBM and the City of Lyon, France, announced information on a pilot project will offer transportation engineers real-time decision support on steps to reduce traffic congestion and enable faster incident response for unexpected events.
The solution proposes to manage traffic congestion proactively by software that learns form experience, so that traffic jams can be reduced by quickly implemented detours and more accurate alternate route suggestions.
Using real-time traffic data, the new analytics and optimization technology can help officials predict outcomes and analyze different scenarios to resolve problems. For example, recommended actions could be adjusting traffic signals to allow cars to detour more quickly and to allow for emergency vehicles to enter, adjusting ramp metering or road closures or changing variable message signs to alert of trouble ahead.
In the pilot project, historical and real-time traffic data from the City of Lyon is combined with advanced analytics and algorithms to help model predicted conditions under both normal and incident conditions, and the resulting impact across the entire network of roads, buses and trams. The system can also be used to estimate drive times and traffic patterns in a region more accurately and in real-time.
Over time, the algorithms are intended to “learn” by incorporating best practices and outcomes from successful plans to fine-tune future recommendations. Additionally, a command centre can develop traffic contingency plans for major events such as large sporting events or concerts.
Currently 'no effective way to manage and find actionable insight to act upon instantaneously'
“Today transportation departments often capture real-time traffic data, but there is no effective way to manage and find actionable insight to act upon instantaneously for the immediate benefit of the traveller,” said Sylvie Spalmacin-Roma, vice president, Smarter Cities Europe, IBM. “With the City of Lyon, we will demonstrate how the transportation management center of the future will use analytics to improve the decision-making process, improve first responder time and get citizens moving more efficiently by better managing traffic.”
The new predictive traffic management technology, named Decision Support System Optimizer (DSSO), combines incident detection, incident impact prediction and propagation, traffic prediction and control plan optimization. It also uses the IBM Data Expansion Algorithm, which can estimate traffic data that it is not available from sensors using descriptive flow models in conjunction with the available real-time traffic data. The new technology is compatible with the IBM Intelligent Operation Center’s Intelligent Transportation solution. IBM's software solutions for cities draw on experience gained from Smarter Cities projects with cities around the world.
IBM and City of Lyon, France to Create Transportation Management Centre of the Future