[Geowanking] Probe based mapping of road network
joshua at burri.to
Thu Nov 30 16:44:28 PST 2006
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
> 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
> *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
> 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?
> 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
> Geowanking mailing list
> Geowanking at lists.burri.to
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