[Geowanking] Probe based mapping of road network

[joshua]

This feels like a clustering problem, to me.

Is there any data that you have to share? Perhaps a netflix-style 
competition might be in order.

Couldn't one analyze this on a per-region segmented datasets? (grid 
partitions or whatever)

You can also probably identify time-of-day and day-of-week traffic flow 
rates, and so on.

This project seems like an enormous amount of fun.

christopher.wilson at daimlerchrysler.com wrote:
>
> After watching this group for a while, thought it would be interesting 
> to bring up a topic I have been working on for several years and see 
> if I can get any help from the geowanking crowd.
>
> *Goal:*  Create highly accurate and complete digital maps of the 
> transportation network suitable for safety of life applications with 
> accuracy commensurate with future GNSS systems (decimeters).  It seems 
> to me that this can only be done through a statistical, probe based, 
> approach since imagery and 'mobile mapping' approaches are error prone 
> with low revisit rates.
>
> *Problem*:  Given a very large set of vehicle PVT (position, velocity, 
> time) information,
> 1) derive the location of the centerline of every lane, along with 
> lane attributes such as direction and ability to cross to the adjacent 
> lane,
> 2) derive the location of all turn restrictions and traffic controls, and
> 3) relate the PVT accuracy of the data to the accuracy of the 
> resulting 'map' for different quantities of data.  
> For extra credit, identify movements within lanes that indicate a 
> vehicle intends to turn, stop, or execute some other maneuver.  Of 
> course, all of these answers must come with a statistical accuracy 
> metric.
>
> *Background*: There are a lot of GPS units in a lot of cars collecting 
> a lot of data on where the cars (roads) are and how they move 
> (controls such as yields and stops).  This data is then thrown away. 
>  If this data can be captured (and there are efforts underway to do 
> this), how does one build a map of the roads and all of the signs and 
> signals that control the motion of vehicles?  I believe that the 
> entire infrastructure that influences the behavior of vehicles is 
> captured in this data, and that, by the central limit theorem, the 
> data has ever increasing (and quantifiable!) accuracy.    This is 
> exactly what is needed for map based transportation safety systems 
> currently under development.   This is one very promising way to 
> address the 40,000+ fatalities/ $200B a year caused by accidents on US 
> roads.
>
> We spent a couple of years looking at this and devised a k-means 
> approach bundling data across the direction of travel to pull out the 
> lanes.  The data could then be grouped by lanes to derive centerlines. 
>  Stop signs and traffic lights were easy, we never got to yields or 
> speed limits.  Our approach was successful, but computationally 
> intensive, and required that one work with the entire data set rather 
> than a Kalman filter approach where data can be incrementally added to 
> improve the solutions validity (or indicate that the world has 
> changed).   We also did not get far on the accuracy metrics.  The key 
> to this problem seems to be grouping vehicles into like groups going 
> from 'A' to 'B', where 'A' and 'B' are any two arbitrary points on the 
> road network with an accuracy of around 30 cm.   We can 'generally' 
> assume that a vehicle is within 30cm of the 'lane center'.  One 
> problem, of course, is that the accuracy of any individual vehicle's 
> position is generally somewhat larger than the lane width.
>
> Does anyone know anybody working this (or similar) problems?
>
> Any ideas on how to approach this from the geo-statistical crowd out 
> there?  We came at this from an AI perspective, and I think a 
> geo-statistical approach might have gone a different direction.
>
> Other thoughts?
>
> -=Chris
>
> PS-  This approach is really promising for getting public, low cost, 
> accurate maps of transportation networks, and yes, there are some 
> serious privacy issues to work through.  There will never be unique 
> identifiers in the data, and we can cut out the first and last mile.
>
> Christopher.Wilson at dcx.com
> 650/845-2579
> ------------------------------------------------------------------------
>
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