On 1/26/2012 12:05 PM, Nicolas Ribot wrote:
On 26 January 2012 17:16, Stephen Woodbridge<[email protected]> wrote:
On 1/26/2012 10:47 AM, Nicolas Ribot wrote:
Hi All,
I have an interesting problem I am trying to solve and would love some
feedback on how to best go about it.
I have road data from two different vendor data sets. But this could also
be
one road network and a GPS track, so I think this is a pretty common use
case.
Assumptions:
o the networks are similar, ie: they have similar roadway coverage
o the two sets might be slightly misaligned, ie: shifted by some amount
o the segments in the two data sets do not have to be broken into
equivalent
segments, ie: one segment in A might be represented my multiple segments
in
B
o segments are not aligned end point wise, ie: a segment in A might go
from
mid-point one segment in B to the midpoint of a connected segment in B
o in many cases I will be working with a set of lines in one set that I
need
to match to the other to select a matching set of lines.
So strategies for matching these:
1. take a segment from A and buffer it, then intersect the data in B and
select the longest intersected object. I can probably throw out any
pieces
smaller than the buffer distance.
2. Do the same but buffer and union the set of lines into a single
multipolygon, and intersect that with the other set.
3. ??? Other ideas?
Thoughts on performance?
Typically I will have a small set (1-20) of segments to compare against a
larger (100K-2M) set. Obvious a spatial index will will be used. But I'm
wondering what is the fast way to do this matching computationally. I
think
I will want to be able to compare 1-200 sets like this every 5 mins as
data
comes in from a feed, while supporting other queries.
With this small input set, maybe st_hausdorffDistance() could be
useful to select good candidates in the larger set.
Hi Nicolas,
Thank you for your insights and suggestions. I have not used
st_hausdorffDistance() and just read up on it. I agree this might be a good
option for initial segment selection.
Stephen,
another thought:
Are your input segments connected and forming a longer linestring you
want to "rebuild" against big dataset, or are they independent objects
?
If the latter, distance search and segment orientation could give good
results, assuming the big dataset segments orientations are
pre-computed:
If the segments are close to each other (in fact, it should be "if all
their vertices are close enough") and the segments orientations are in
a close range, then chances are good you found the right target
segment.
Nicolas,
I am still waiting on the actual data. The input is a traffic disruption
feed with ids to one dataset and I need to map the identified segments
to the equivalent segments in a Navteq data set. I suspect that I will
have a combination of connected segments that form a sub-network and not
a single path. But some of the sample feed data I do have references a
single segment, like the location of an accident. In other cases, like a
major construction project that affects multiple segments on multiple
streets it will be a sub-net.
-Steve
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