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.
[EMAIL PROTECTED] 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.
[EMAIL PROTECTED]
650/845-2579
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