Predictive maintenance tool to help drivers avoid congestion
Researchers at IBM are collaborating with partners at the California Department of Transportation (Caltrans) and California Center for Innovative Transportation (CCIT) to develop an intelligent system that will help commuters avoid congestion.
By joining forces, IBM, Caltrans and the CCIT team hope to provide drivers with valuable predictive information on what traffic patterns are likely to look like — even before they get in their vehicles — rather than discover what has already happened once in them.
To develop the system, the researchers will use a learning and predictive-analytics tool called the Traffic Prediction Tool (TPT). Developed by IBM Research, the tool continuously analyses congestion data from existing sensors in roads, toll booths, bridges and intersections.
That data will be correlated with positional information from GPS sensors in participating people’s mobile phones to learn their preferred travel days and routes.
In use, the system will then deliver alerts via email or text message on the status of the driver’s typical commute before any trip begins.
The alerts will enable drivers to plan and share alternative travel routes, improve traveller 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.