As readers of this blog know, I am always on the lookout for examples of Monday Morning Analytics in action. I stumbled on an unusual and neat example recently.
I was in Chicago last week to give a talk on analytics at Navteq, possibly the world’s largest provider of mapping data and related services. I heard that Navteq map data is used 100m times a day; for example, if you use a Garmin GPS device or a mapping application on a Nokia phone, you are using Navteq data.
I had several interesting conversations about how location data can be profitably used in a variety of contexts, especially in retailing. I heard some great examples of creative and clever location-based services that are likely to appear in the next couple of years, particularly on mobile phones (the marriage of location data with mobile phones has already produced interesting progeny like Foursquare and Gowalla). But what caught my attention was an example that had nothing to do with mobile phones. It involves the cruise-control system in trucks!
All trucks have cruise control. When a truck driver is on an interstate highway and turns on cruise control, the system maintains the desired speed, accelerating and braking as needed.
But this sort of simple cruise control mechanism is not particularly fuel-efficient. It will consume a lot of gas to accelerate up a small hill (since it is trying to be at the desired speed) and then waste all that kinetic energy by braking while coming down the hill on the other side (since it doesn’t want to exceed the desired speed).
So far so good. Then, somebody, somewhere asked this question:
“Most trucks have GPS with the underlying map database on-board. From the map data, we know what’s ahead on the road. We know the ups-and-downs of the terrain and curves in the road. Why can’t we use this knowledge of what lies ahead to make the cruise control smarter?”
Brilliant!
They acted on this insight and created a smarter cruise-control system with “analytics inside”. This system uses the detailed map data to accelerate and brake in such a way that fuel consumption is minimized. When a hill is approaching, the system will not accelerate as much as before since it knows it will be going downhill soon and will have plenty of kinetic energy to hit the desired speed. When a curve is approaching, the system will take its foot off the gas pedal and slow down rather than wait for the driver to hit the brakes (this, of course, is a great safety feature as well).
I don’t have data on the number of miles traveled annually by freight trucks but I am sure it is not a small number. Making those trucks a tad more fuel-efficient is certain to have a big positive impact on both operating costs and the environment.
In my opinion, this is a neat example of Monday Morning Analytics. The system uses data to make a better decision (as opposed to simply identifying an “insight”). In fact, it goes one step further since it executes the better decision automatically without consulting the human decision-maker.
All the key ingredients of a modern decision-support system are present:
- data: the truck’s precise location (thanks to the GPS) and the detailed map data. Note that simple map data isn’t enough. The data needs to include features such as terrain, road curves etc. Navteq has developed very cool technology to collect all this information and more.
- prediction: the detailed map data is used to “predict” what lies ahead. Strictly speaking, they are not predicting as much as looking up the relevant data but the notion of using map data from the immediate horizon of the truck to project fuel-consumption and how it changes with different accelerate/brake decisions feels like predictive modeling.
- optimization: the system finds the set of accelerate/brake/coast decisions that minimize fuel consumption while honoring the driver’s desired speed constraint. Textbook definition of optimization.
Nicely done!
