About prize baring contests

Competition with prizes are an amazing thing. If you are not sure of that, I urge you to listened to Peter Diamandis talk about his experience with the X prize (start listening at minute 11:40):

At short – prizes can give up to 1 to 50 ratio of return on investment of the people giving funding to the prize. The money is spent only when results are achieved. And there is a lot of value in terms of public opinion and publicity. And the best of all (for the promoter of the competition) – prizes encourage people to take risks (at their own expense) in order to get results done.

All of that said, I look at prize baring competition as something worth spreading, especially in cases where the results of the winning team will be shared with the public.

About the IEEE ICDM Contest

The IEEE ICDM Contest (“Road Traffic Prediction for Intelligent GPS Navigation”), seems to be one of those cases. Due to a polite request, I am republishing here the details of this new competition, in the hope that some of my R colleagues will bring the community some pride

Introduction

Data mining competition affiliated with IEEE International Conference on Data Mining 2010 (ICDM), Sydney, Australia, Dec 14-17. The task is to predict city traffic based on simulated historical measurements or real-time stream of notifications sent by individual drivers from their GPS navigators. Prizes worth $5,000 will be awarded to the winners.

Road Traffic Prediction for Intelligent GPS Navigation

Over the last century, number of cars engaged in vehicular traffic in cities has increased rapidly, causing many difficulties for all citizens: traffic jams, large and unpredictable communication delays, pollution etc. Excessive traffic became a civilization problem that affects everyone who lives in a city of 50,000 or larger, anywhere in the world. Complexity of processes that stand behind traffic flow is so large, that only data mining algorithms – from the domains of structure mining, graph mining, data streams, large-scale and temporal data mining – may bring efficient solutions for these problems. With the proposed competition, we want to ask researchers to devise the best possible algorithms that tackle problems of traffic flow prediction, for the purpose of intelligent driver navigation and improved city planning.
There are Tyree independent tasks:

• Traffic (link). Traffic congestion prediction, in an elementary setup of time series forecasting: a series of measurements from 10 selected road segments is given and the goal is to make short-term predictions of future values based on historical ones. This task is intended as an introductory one, simpler than the other two.
• Jams (link). Modeling the process of traffic jams formation during morning peak in the presence of roadworks, based on initial information about jams broadcast by radio stations. Input data contain identifiers of road segments closed due to roadworks, accompanied by a sequence of segments where the first jams occurred. The algorithm should predict a sequence of segments where next jams will occur in the nearest future.
• GPS (link). Traffic reconstruction and prediction based on real-time information from individual drivers. Input data consist of a stream of notifications from 1% of vehicles about their current GPS locations in the city road network, sent every 10 seconds. The algorithm receives this stream and predicts traffic congestion on selected road segments for the next 30 minutes. Large volumes of data are involved in this task, requiring the use of scalable data mining methods.

Everyone is welcome to participate. Competition starts now and will last till September 6th, 2010. More details on: http://tunedit.org/challenge/IEEE-ICDM-2010