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I have a dataset which is in the form of ( Time , Car ID, Speed at that moment, Lat,Long) and I need to do some machine learning on the data to answer the following questions :

  1. Best route from point A to point B
  2. The speed at some point ( which can be on any segment) .

I am having a few problems because I have never dealt with this kind of data before ( I mostly worked with text before this ) :

  1. The data is Hyderabad specific. I want to divide Hyderabad into segments but how do I go about dividing it based on points ? If I use 500m as minimum length and keep dividing , there can be many twists and turns which will go missing :(
  2. If I get a random point , how do I decide which segment it falls on ? For example, I have two segments , each having start point ( lat,long) and end point ( lat',long') . If i get a third point , (l1,l2), how do I decide which segment it falls under ?

I actually have more questions, but if these get solved first, it would be awesome.

Thanks a lot

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Why don't you download OpenStreetMap data for Hyderabad and snap your lat/lon points on the OSM street data? I guess it would make your analysis much easier to do.

share|improve this answer
I saw openstreetmap but the problem is kinda more finer than that. We can map our points on google maps API. What we need is better mapping for random points that will come up on the data. – crazyaboutliv Jul 1 '11 at 12:42
I wasn't talking about mapping (i.e. showing the data). I was talking about using OSM vector data to determine the nearest street a random point belongs to. I'm not sure this is something Google Maps API provides. – Igor Brejc Jul 1 '11 at 15:41
Ohh ! I am sorry I did not come across that. I am checking the link right now. Sorry for the late reply. – crazyaboutliv Jul 2 '11 at 7:29

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