Crowd-sourcing is one of those buzz words going around. Crowd-sourcing is the act of enlisting a crowd of people into providing useful data in a shared space. For example digg.com is a crowd sourced ranking of web page popularity. Of course crowd sourced data can be skewed by the nature of the crowd of people who provide the data, as can be readily seen by the slant of digg.com’s community interest.
With Waze the data is driving conditions on the road network as provided by the Waze users. A Waze user downloads a special application into their phone, leaves the phone running that app while driving, and the app uploads GPS coordinates and driving conditions to the Waze service. For example the Waze service can detect traffic jams by noting the speeds of enough cars driving the same road at the same time.
In theory a service like this would be ‘green’ if someone can know ahead of time about a traffic jam and choose an alternative route. By avoiding a traffic jam not only does the driver experience less of the traffic jam angst, they should use less fuel because of less low speed bumper-bumper driving.
The web site includes a live updated map of traffic conditions, a live ticker of traffic conditions, and community chatter about problems in the map.
However this map is only as good as the size of the community providing data. Looking at the SF Bay Area map the only traffic conditions reported are in San Francisco. Looking at the US-wide map shows only a handful of cities reporting traffic conditions. Clearly the service does not at this time have enough users to provide a useful comprehensive set of data.
One might think, how does this differ from the traffic conditions on Google Maps? On Google Maps the traffic data is only for major highways and roads, whereas with Waze the traffic conditions are for any road Waze users traverse. Additionally the Google Maps traffic data is only in cities where Google provides the service, whereas with Waze it is available in any city containing enough Waze users.
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