Watch this space: Your future; now available in Real-Time
Imagine you have an automatically and real-time updated agenda - it continuously adapts your schedule to meetings taking longer, predicts and updates in real-time your travel-time to the next meetings and will adapt your schedule because it 'knows' that typically any meeting with your best client always takes 30 minutes longer than you originally plan it for.
A proof of concept conducted by the Atos Scientific Community looked at this aspect of predictability and took the data of the traffic in the city of Berlin to see if it was possible to do real time traffic forecasting (RTTF). The result is in a white paper that is being published.
"RTTF enables a prediction (within 1 minute) of sensor data streams for the immediate future (up to four hours) and provides traffic condition classification for the upcoming time period based on the forecasted data."
"The forecast provides a suitable time span for proactively managing upcoming incidents even before they appear."
The team took a radical different approach to the challenges of today’s traffic management. Instead of proposing another reactive traffic management IT system with some smart analytics, the team targeted successfully a proactive traffic management approach which provides analytics solutions to predict critical events in advance before they appear. Using historic data and artificial neuron network technology, predictions are created for the intermediate future and utilized to determine the traffic status of the upcoming next four hours. Based on that information, actions can be taken proactively to mitigate or avoid future upcoming events. Utilizing the software and bringing in data scientists with an understanding of the context was the next step. This helped in defining the right parameters and a pattern based strategy (PBS) in place.
“Being able to identify patterns out of the existing data, model them into patterns and come up with a system that can provide reliable predictions is a remarkable achievement in itself, but the true value of PBS is being able to apply such capabilities to strategy definition and decision making.”
Working with the subject matter experts the team identified multiple models that were then consequently implemented in the software. The models are important, they avoid that you are trapped into simplification; when a car is driving slowly, it can be because of a traffic jam, but it can also be an older person driving more carefully.
By introducing the concept of 'flow' - the number of vehicles passing a sensor each hour - the team could identify 4 different states, which were in themselves also parameterized by looking at road capacity, speed limits, etc. This information is then fed into a look-up table based complex event processing engine in order to predict, within 1 minute, the traffic situation at given locations.
Because in real-life the historic data is continuously refreshed with the actual events of the past time, the system will be able to predict in real-time the situation on the road.
The proof of concept clearly showed that a self-learning system, combined with a complex event processing unit and the help of some subject matter expert data scientist can accurately predict the future - the white paper shows this in some great details.
"Real Time Traffic Forecasting is an excellent example of how data sources and identified patterns can be exploited to gain insights and to develop proactive strategies to deal with upcoming events and incidents. It enables a short term view into the future which is long enough to act on predicted incidents rather than react on occurring ones"
For me this proof of concept shows the benefits of data analytics in everyday life, and I am looking forward to this future.
Update! The white paper is finally available here.